Dependency graph in data-driven model

ABSTRACT

The inference of a dependency graph that represents a graph of solves that leads from input model parameter(s) to output model parameters using analytics. In one embodiment, the dependency graph is part of visually driven analytics in which the output model parameter(s) are used to formulate data-drive scenes. As the identity of the input and/or output model parameter(s) change, or as the analytics themselves change, the dependency graph may also change. This might trigger a resolve of the analytics. In one embodiment, the intermediate parameters involved in the dependency graph may be viewed and evaluated by the user.

BACKGROUND

Often, the most effective way to convey information to a human being isvisually. Accordingly, millions of people work with a wide range ofvisual items in order to convey or receive information, and in order tocollaborate. Such visual items might include, for example, conceptsketches, engineering drawings, explosions of bills of materials,three-dimensional models depicting various structures such as buildingsor molecular structures, training materials, illustrated installationinstructions, planning diagrams, and so on.

More recently, these visual items are constructed electronically using,for example, Computer Aided Design (CAD) and solid modelingapplications. Often these applications allow authors to attach data andconstraints to the geometry. For instance, the application forconstructing a bill of materials might allow for attributes such as partnumber and supplier to be associated with each part, the maximum anglebetween two components, or the like. An application that constructs anelectronic version of an arena might have a tool for specifying aminimum clearance between seats, and so on.

Such applications have contributed enormously to the advancement ofdesign and technology. However, any given application does have limitson the type of information that can be visually conveyed, how thatinformation is visually conveyed, or the scope of data and behavior thatcan be attributed to the various visual representations. If theapplication is to be modified to go beyond these limits, a newapplication would typically be authored by a computer programmer whichexpands the capabilities of the application, or provides an entirely newapplication. Also, there are limits to how much a user (other than theactual author of the model) can manipulate the model to test variousscenarios.

BRIEF SUMMARY

Embodiments described herein relate to the inference of a dependencygraph that represents a graph of solves that leads from input modelparameter(s) to output model parameters using analytics. In oneembodiment, the dependency graph is part of visually driven analytics inwhich the output model parameter(s) are used to formulate data-drivescenes. As the identity of the input and/or output model parameter(s)change, or as the analytics themselves change, the dependency graph mayalso change. This might trigger a resolve of the analytics. In oneembodiment, the intermediate parameters involved in the dependency graphmay be viewed and evaluated by the user.

This Summary is not intended to identify key features or essentialfeatures of the claimed subject matter, nor is it intended to be used asan aid in determining the scope of the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the manner in which the above-recited and otheradvantages and features can be obtained, a more particular descriptionof various embodiments will be rendered by reference to the appendeddrawings. Understanding that these drawings depict only sampleembodiments and are not therefore to be considered to be limiting of thescope of the invention, the embodiments will be described and explainedwith additional specificity and detail through the use of theaccompanying drawings in which:

FIG. 1 illustrates an environment in which the principles of the presentinvention may be employed including a data-driven composition frameworkthat constructs a view composition that depends on input data;

FIG. 2 illustrates a pipeline environment that represents one example ofthe environment of FIG. 1;

FIG. 3 schematically illustrates an embodiment of the data portion ofthe pipeline of FIG. 2;

FIG. 4 schematically illustrates an embodiment of the analytics portionof the pipeline of FIG. 2;

FIG. 5 schematically illustrates an embodiment of the view portion ofthe pipeline of FIG. 2;

FIG. 6 schematically illustrates a data stream object that is capable ofenumerating all or a subset of the elements of the data stream;

FIG. 7 illustrates a rendering of a view composition that may beconstructed by the pipeline of FIG. 2;

FIG. 8 illustrates a flowchart of a method for generating a viewcomposition using the pipeline environment of FIG. 2;

FIG. 9 illustrates a flowchart of a method for regenerating a viewcomposition in response to user interaction with the view compositionusing the pipeline environment of FIG. 2;

FIG. 10 schematically illustrates the solver of the analytics portion ofFIG. 4 in further detail including a collection of specialized solvers;

FIG. 11 illustrates a flowchart of the solver of FIG. 10 solving forunknown model parameters by coordinating the actions of a collection ofspecialized solvers;

FIG. 12 schematically illustrates a solver environment that representsan example of the solver of FIG. 10;

FIG. 13 illustrates a flowchart of a method for using the solverenvironment of FIG. 12 to solve for model analytics;

FIG. 14 illustrates a flowchart of a method for using the solverenvironment of FIG. 10 to solve for a model variable;

FIG. 15 schematically illustrates an embodiment of a solver environment;

FIG. 16 illustrates a flowchart of a method that may be performed by thesolver environment shown in FIG. 15;

FIG. 17 illustrates a rendering of an integrated view composition thatextends the example of FIG. 7;

FIG. 18A illustrates a view composition in which several visual itemsare adorned with visual cues representing that the corresponding visualitem may be interacted with the perform scrolling;

FIG. 18B illustrates the view composition of FIG. 18A after thescrolling interaction;

FIG. 19A illustrates a view composition in which zooming interactivityis enabled;

FIG. 19B illustrates the view composition of FIG. 19A after a zoom-ininteractivity;

FIG. 20 illustrates a view composition in the form of a United Statesmap with the elevation of each state representing data, and with some ofthe state visual items including visual cues indicating possibilityinteractivity with that state visual item;

FIG. 21 illustrates a view composition in the form of a combinedinterrelated pie chart and bar chart that include visual cues showingpossible interactivity;

FIG. 22 illustrates a view composition in form of a hurricane pathprogress diagram in which various visual cues identify interactivity;

FIG. 23 illustrates a flowchart of a method for providing interactivityin a view composition;

FIG. 24 illustrates a user interface in which various visual items maybe integrated and merged;

FIG. 25 illustrate a first stage in integration in which a mere helix isshown as a shape upon which data will be mapped;

FIG. 26 illustrates a second stage in integration in which the helix ofFIG. 25 is tied to a data series;

FIG. 27 illustrate a third stage in integration in which the helix ofFIG. 25 is tied to two data series;

FIG. 28 illustrates a final stage in integration in which the helix ofFIG. 25 is tied to the illustrated chart.

FIG. 29 illustrates a visualization of a shelf layout and representsjust one of countless applications that the principles described hereinmay apply to;

FIG. 30 illustrates a visualization of an urban plan that the principlesdescribed herein may also apply to;

FIG. 31 illustrates a conventional visualization comparing children'seducation, that the principles of the present invention may apply tothereby creating a more dynamic learning environment;

FIG. 32 illustrates a conventional visualization comparing populationdensity, that the principles of the present invention may apply tothereby creating a more dynamic learning environment;

FIG. 33 illustrates a visualization of view components applied to afirst parametric target;

FIG. 34 illustrates a visualization of the view components shown in FIG.33 applied to a second parametric target; and

FIG. 35 illustrates a taxonomy environment in which the taxonomycomponent of FIG. 2 may operate;

FIG. 36 illustrates an example of a taxonomy of the member items of FIG.35;

FIGS. 37A through 37C show three examples of taxonomies of relatedcategories;

FIG. 38 illustrates a member item that includes multiple properties;

FIG. 39 illustrates a domain-specific taxonomy and represents oneexample of the domain-specified taxonomies of FIG. 35;

FIG. 40 illustrates a flowchart of a method for navigating and usinganalytics;

FIG. 41 illustrates a flowchart of a method for searching usinganalytics;

FIG. 42 illustrates a computing system that represents an environment inwhich the composition framework of FIG. 1 (or portions thereof) may beimplemented; and

FIG. 43 illustrates a flowchart of a method for inferring a dependencygraph.

DETAILED DESCRIPTION

FIG. 1 illustrates a visual composition environment 100 that usesdata-driven analytics and visualization of the analytical results. Theenvironment 100 (also called hereinafter a “pipeline”) includes acomposition framework 110 that performs logic that is performedindependent of the problem-domain of the view construction 130. Forinstance, the same composition framework 110 may be used to composeinteractive view compositions for city plans, molecular models, groceryshelf layouts, machine performance or assembly analysis, or otherdomain-specific renderings. As will be described, the analytics may beused to search and explore in various scenarios. First, however, thebasic composition framework 110 will be described in detail.

The composition framework 110 performs analytics 121 usingdomain-specific data 120 that is taxonomically organized in adomain-specific way to construct the actual view construction 130 (alsocalled herein a “view composition”) that is specific to the domain.Accordingly, the same composition framework 110 may be used to constructview compositions for any number of different domains by changing thedomain-specific data 120, rather than having to recode the compositionframework 110 itself. Thus, the composition framework 110 of thepipeline 100 may apply to a potentially unlimited number of problemdomains, or at least to a wide variety of problem domains, by alteringdata, rather than recoding and recompiling. The view construction 130may then be supplied as instructions to an appropriate 2-D or 3-Drendering module. The architecture described herein also allows forconvenient incorporation of pre-existing view compositions as buildingblocks to new view compositions. In one embodiment, multiple viewcompositions may be included in an integrated view composition to allowfor easy comparison between two possible solutions to a model.

FIG. 2 illustrates an example architecture of the composition framework110 in the form of a pipeline environment 200. The pipeline environment200 includes, amongst other things, the pipeline 201 itself. Thepipeline 201 includes a data portion 210, an analytics portion 220, anda view portion 230, which will each be described in detail with respectto subsequent FIGS. 3 through 5, respectively, and the accompanyingdescription. For now, at a general level, the data portion 210 of thepipeline 201 may accept a variety of different types of data andpresents that data in a canonical form to the analytics portion 220 ofthe pipeline 201. The analytics portion 220 binds the data to variousmodel parameters, and solves for the unknowns in the model parametersusing model analytics. The various parameter values are then provided tothe view portion 230, which constructs the composite view using thosevalues of the model parameters.

The pipeline environment 200 also includes an authoring component 240that allows an author or other user of the pipeline 201 to formulateand/or select data to provide to the pipeline 201. For instance, theauthoring component 240 may be used to supply data to each of dataportion 210 (represented by input data 211), analytics portion 220(represented by analytics data 221), and view portion 230 (representedby view data 231). The various data 211, 221 and 231 represent anexample of the domain-specific data 120 of FIG. 1, and will be describedin much further detail hereinafter. The authoring component 240 supportsthe providing of a wide variety of data including for example, dataschemas, actual data to be used by the model, the location or range ofpossible locations of data that is to be brought in from externalsources, visual (graphical or animation) objects, user interfaceinteractions that can be performed on a visual, modeling statements(e.g., views, equations, constraints), bindings, and so forth.

In one embodiment, the authoring component is but one portion of thefunctionality provided by an overall manager component (not shown inFIG. 2, but represented by the composition framework 110 of FIG. 1). Themanager is an overall director that controls and sequences the operationof all the other components (such as data connectors, solvers, viewers,and so forth) in response to events (such as user interaction events,external data events, and events from any of the other components suchas the solvers, the operating system, and so forth).

The authoring component 240 also includes a search tool 242 that allowsfor searches to be performed. The search tool 242 is able to draw on thedata-driven analytical capabilities of the pipeline 201 in order toperform complex search operations. For instance, in some cases, one ormore parameters of the search might first need to be solved for in orderto complete the search.

As an example, suppose that the data driven analytics is a city map, andsome of the analytics are capable of solving for the typical noise levelat particular coordinates in the city. In that case, a person searchingfor residential real estate may perform a search not just on the typicalsearch parameters of square footage, price range, number of rooms, andso forth, but may also search on analytically intensive parameters. Forinstance, while there are unlimited ways that the principles may beapplied, a selected few diverse examples of how a flexible search forreal estate might be accomplished will now be provided.

As part of specifying search parameters, the user might indicate adesire for any one or more of the following or other search items:

-   -   1) only those homes that experience average noise levels below a        certain upper limit;    -   2) only those homes that have a cumulative 300 minute commute        time or less to the user's work and gym on each of Monday        through Thursday,    -   3) only those homes that experience less than a certain        threshold of traffic on any road within one fifth of a mile and        which are predicted over the next 10 years to remain below that        level of traffic,    -   4) only those homes that are not in the shadow of a mountain        after 9:15 am at any point in the year,    -   5) only those homes that have sufficient existing trees on the        property such that in 10 years time, the trees would shade at        least 50% of the area of the roof,    -   6) and so forth.        Such real estate searches are not readily achievable using        conventional technology. In each of these examples, the        requested home search data may not exist, but the principles        described herein may allow the search data for a variety of        customized parameters to be generated on the fly as the search        is being performed. Also, the search may take advantage of any        search data that has been previously solved for. This enables a        wide variety of search and exploration capabilities for the        user, and opens up a whole new way of exploring a problem to be        solved. More regarding the underlying data-driven analytics        mechanisms that support these types of searches will now be        described.

Traditionally, the lifecycle of an interactive view compositionapplication involves two key times: authoring time, and use time. Atauthoring time, the functionality of the interactive view compositionapplication is coded by a programmer to provide an interactive viewcomposition that is specific to the desired domain. For instance, theauthor of an interior design application (e.g., typically, a computerprogrammer) might code an application that permits a user to perform afinite set of actions specific to interior designing.

At use time, a user (e.g., perhaps a home owner or a professionalinterior designer) might then use the application to perform any one ormore of the set of finite actions that are hard coded into theapplication. In the interior design application example, the user mightspecify the dimensions of a virtual room being displayed, add furnitureand other interior design components to the room, perhaps rotate theview to get various angles on the room, set the color of each item, andso forth. However, unless the user is a programmer that does not mindreverse-engineering and modifying the interior design application, theuser is limited to the finite set of actions that were enabled by theapplication author. For example, unless offered by the application, theuser would not be able to use the application to automatically figureout which window placement would minimize ambient noise, how the roomlayout performs according to Feng Shui rules, or minimize solar heatcontribution.

However, in the pipeline environment 200 of FIG. 2, the authoringcomponent 240 is used to provide data to an existing pipeline 201, whereit is the data that drives the entire process from defining the inputdata, to defining the analytical model, to defining how the results ofthe analytics are visualized in the view composition. Accordingly, oneneed not perform any coding in order to adapt the pipeline 201 to anyone of a wide variety of domains and problems. Only the data provided tothe pipeline 201 is what is to change in order to apply the pipeline 201to visualize a different view composition either from a differentproblem domain altogether, or to perhaps adjust the problem solving foran existing domain. The pipeline environment 200 may also include ataxonomy component 260 that organizes, categorizes, and relates datathat is provided to the pipeline 201. The taxonomy component 260 may besensitive to the domain. Thus, an interior design domain, a road designdomain, an architecture domain, a Feng Shui domain may each have adifferent taxonomy that may be used to navigate through the data. Thiscan be helpful since, as will be described below, there may beconsiderable data available to sift through due to composition activityin which the data set that is made available to the pipeline environment200 may be ever increasing.

Further, since the data can be changed at use time (i.e., run time), aswell as at author time, the model can be modified and/or extended atruntime. Thus, there is less, if any, distinction between authoring amodel and running the model. Because all authoring involves editing dataitems and because the software runs all of its behavior from data, everychange to data immediately affects behavior without the need forrecoding and recompilation.

The pipeline environment 200 also includes a user interaction responsemodule 250 that detects when a user has interacted with the displayedview composition, and then determines what to do in response. Forexample, some types of interactions might require no change in the dataprovided to the pipeline 201 and thus require no change to the viewcomposition. Other types of interactions may change one or more of thedata 211, 221, or 231. In that case, this new or modified data may causenew input data to be provided to the data portion 210, might require areanalysis of the input data by the analytics portion 220, and/or mightrequire a re-visualization of the view composition by the view portion230.

Accordingly, the pipeline 201 may be used to extend data-drivenanalytical visualizations to perhaps an unlimited number of problemdomains, or at least to a wide variety of problem domains. Furthermore,one need not be a programmer to alter the view composition to address awide variety of problems. Each of the data portion 210, the analyticsportion 220 and the view portion 230 of the pipeline 201 will now bedescribed with respect to the data portion 300 of FIG. 3, the analyticsportion 400 of FIG. 4, and the view portion 500 of FIG. 5, in thatorder. In addition, the taxonomy of the data is specific to the domain,thus allowing the organization of the data to be more intuitive to thoseoperating in the domain. As will be apparent from FIGS. 3 through 5, thepipeline 201 may be constructed as a series of transformation componentswhere they each 1) receive some appropriate input data, 2) perform someaction in response to that input data (such as performing atransformation on the input data), and 3) output data which then servesas input data to the next transformation component.

The pipeline 201 may be implemented on the client, on the server, or mayeven be distributed amongst the client and the server withoutrestriction. For instance, the pipeline 201 might be implemented on theserver and provide rendering instructions as output. A browser at theclient-side may then just render according to the rendering instructionsreceived from the server. At the other end of the spectrum, the pipeline201 may be contained on the client with authoring and/or use performedat the client. Even if the pipeline 201 was entirely at the client, thepipeline 201 might still search data sources external to the client forappropriate information (e.g., models, connectors, canonicalizers,schemas, and others). There are also embodiments that provide a hybridof these two approaches. For example, in one such hybrid approach, themodel is hosted on a server but web browser modules are dynamicallyloaded on the client so that some of the model's interaction and viewinglogic is made to run on the client (thus allowing richer and fasterinteractions and views).

FIG. 3 illustrates just one of many possible embodiments of a dataportion 300 of the pipeline 201 of FIG. 2. One of the functions of thedata portion 300 is to provide data in a canonical format that isconsistent with schemas understood by the analytics portion 400 of thepipeline discussed with respect to FIG. 4. The data portion includes adata access component 310 that accesses the heterogeneous data 301. Theinput data 301 may be “heterogeneous” in the sense that the data may(but need not) be presented to the data access component 310 in acanonical form. In fact, the data portion 300 is structured such thatthe heterogeneous data could be of a wide variety of formats. Examplesof different kinds of domain data that can be accessed and operated onby models include text and XML documents, tables, lists, hierarchies(trees), SQL database query results, BI (business intelligence) cubequery results, graphical information such as 2D drawings and 3D visualmodels in various formats, and combinations thereof (i.e., a composite).Further, the kind of data that can be accessed can be extendeddeclaratively, by providing a definition (e.g., a schema) for the datato be accessed. Accordingly, the data portion 300 permits a wide varietyof heterogeneous input into the model, and also supports runtime,declarative extension of accessible data types.

In one embodiment, the data access portion 300 includes a number ofconnectors for obtaining data from a number of different data sources.Since one of the primary functions of the connector is to placecorresponding data into canonical form, such connectors will often bereferred to hereinafter and in the drawings as “canonicalizers”. Eachcanonicalizer might have an understanding of the specific ApplicationProgram Interfaces (API's) of its corresponding data source. Thecanonicalizer might also include the corresponding logic for interfacingwith that corresponding API to read and/or write data from and to thedata source. Thus, canonicalizers bridge between external data sourcesand the memory image of the data.

The data access component 310 evaluates the input data 301. If the inputdata is already canonical and thus processable by the analytics portion400, then the input data may be directly provided as canonical data 340to be input to the analytics portion 400.

However, if the input data 301 is not canonical, then the appropriatedata canonicalization component 330 is able to convert the input data301 into the canonical format. The data canonicalization components 330are actually a collection of data canonicalization components 330, eachcapable of converting input data having particular characteristics intocanonical form. The collection of canonicalization components 330 isillustrated as including four canonicalization components 331, 332, 333and 334. However, the ellipsis 335 represents that there may be othernumbers of canonicalization components as well, perhaps even fewer thanthe four illustrated.

The input data 301 may even include a canonicalizer itself as well as anidentification of correlated data characteristic(s). The data portion300 may then register the correlated data characteristics, and providethe canonicalization component to the data canonicalization componentcollection 330, where it may be added to the available canonicalizationcomponents. If input data is later received that has those correlatedcharacteristics, the data portion 310 may then assign the input data tothe correlated canonicalization component. Canonicalization componentscan also be found dynamically from external sources, such as fromdefined component libraries on the web. For example, if the schema for agiven data source is known but the needed canonicalizer is not present,the canonicalizer can be located from an external component library,provided such a library can be found and contains the needed components.The pipeline might also parse data for which no schema is yet known andcompare parse results versus schema information in known componentlibraries to attempt a dynamic determination of the type of the data,and thus to locate the needed canonicalizer components.

Alternatively, instead of the input data including all of thecanonicalization components, the input data may instead provide atransformation definition defining canonicalization transformations. Thecollection 330 may then be configured to convert that transformationsdefinition into a corresponding canonicalization component that enforcesthe transformations along with zero or more standard defaultcanonicalization transformation. This represents an example of a case inwhich the data portion 300 consumes the input data and does not providecorresponding canonicalized data further down the pipeline. In perhapsmost cases, however, the input data 301 results in correspondingcanonicalized data 340 being generated.

In one embodiment, the data access component 310 may be configured toassign input data to the data canonicalization component on the basis ofa file type and/or format type of the input data. Other characteristicsmight include, for example, the source of the input data. A defaultcanonicalization component may be assigned to input data that does nothave a designated corresponding canonicalization component. The defaultcanonicalization component may apply a set of rules to attempt tocanonicalize the input data. If the default canonicalization componentis not able to canonicalize the data, the default canonicalizationcomponent might trigger the authoring component 240 of FIG. 2 to promptthe user to provide a schema definition for the input data. If a schemadefinition does not already exist, the authoring component 240 mightpresent a schema definition assistant to help the author generate acorresponding schema definition that may be used to transform the inputdata into canonical form. Once the data is in canonical form, the schemathat accompanies the data provides sufficient description of the datathat the rest of the pipeline 201 does not need new code to interpretthe data. Instead, the pipeline 201 includes code that is able tointerpret data in light of any schema that is expressible an accessibleschema declaration language.

One type of data is a data stream object, such as the data stream object600 illustrated and described with respect to FIG. 6. The data streamobject 600 includes an enumeration module 601 that is capable ofenumerating a certain range within a data stream 602 that is associatedwith the data stream object. That range may in fact include the entirerange of the data stream 602. The data stream 602 may be an actualinfinite data stream (such as, for example, a list of all prime numbersor some other infinite algebraic expression). On the other hand, thedata stream 602 may be a “pseudo-infinite” or a “partiallypseudo-infinite” data stream. In this description and in the claims, a“pseudo-infinite” data steam is a data stream that is too large to fitentirely with the volatile memory of the computing system that ishandling the data stream object 600. A “partially pseudo-infinite” datastream is defined as a data stream that would occupy at least half ofthe volatile memory of the computing system that is handling the datastream object. The enumeration module 601 might enumerate only a portionof the data stream in response to a request from an external module,such as, for example, the data portion 210, the analytics portion 220,or the view portion 230 of FIG. 2. The enumeration module 601 mightrequire enumeration beginning at the first elements of the data stream.On the other hand, the enumeration module 601 may allow enumerationbeginning at any other portion of the data stream (i.e., may allowenumeration mid-stream) without first enumerating from the beginning ofthe data stream.

The data stream object 600 may be capable of identifying the requestedportion(s) of the data stream based on properties associated with therequested member elements of the data stream. In that case, the datastream object might include logic that processes the request andidentifies all member elements of the data stream that have therequested properties. For instance, the request might be for allelements of a city that are visible from an elevation of 1000 feet,looking downward at a particular longitudinal and latitudinalcoordinate. On the other hand, the data stream object may also becapable of responding to express requests for member elements.

As an example, the pseudo-infinite data stream could be a description ofa real or imaginary city including a description of all items withinthat city, including a description of every conceivable level of detailof every one of these items. That information might simply be too muchto represent in memory all at once. The data stream object 600 may thusoffer up only the relevant information needed to render the currentview. For instance, if the city is being viewed at an elevation of 100miles, perhaps only the boundary information of the city is relevant. Ifa portion of the city is being viewed at an elevation of ten miles,perhaps only the information about the larger objects (such as airports,parks, reservoirs, and the like might be offered up, and only if withinthe view). If a portion of the city is being viewed at an elevation ofone mile, perhaps the information necessary to render streets andbuildings that are within the view are offered up by the data streamobject. If a portion of the city is being viewed at an elevation of onethousand feet, perhaps information necessary to render more detail ofthe streets (i.e., the number of lanes, the names of the street, arrows,etc.), and more detail on the buildings (texture and windows position,etc.) might be offered up. Naturally, as this zoom in operation occurs,the information pertains to a smaller and smaller geographical area. Ata one hundred foot view, the data stream might offer up informationregarding shrubbery, manholes, gutters, steps, vending machines,cross-walks and the like. That amount of data might be difficult for aconventional computer to keep in memory for an entire city.

As another example, the pseudo-infinite data stream might literally beinfinite, as in the case of a fractal. A fractal is mathematicallydefined such that no matter how much one zooms in on a portion of thefractal, a repeating shape always comes into view. One can always zoomin further, and further, infinitum. The same thing applies for zoomingout. It would take an infinite amount of information to literallyrepresent the geometry of such a fractal. However, the data streamobject might have a concept for how the fractal is mathematicallydefined. If the data stream object is asked to produce a fractal at aparticular level of detail, the data stream object may then calculatethe data that applies to that particular level of detail, and offer thatdata up.

Thus, the pseudo-infinite data object is able to generate requestedranges of data either by evaluating an expression that defines thedetail and/or by accessing data external to the data stream object. Suchranges are examples of the hierarchical data that is provided to theanalytics portion 400 of the pipeline 201. In one embodiment, theprotocol for communicating with the data stream object may be the sameregardless of how large the data stream itself is. Thus, an author mightthus test a model on a data stream object that is associated with asmaller data stream object. Then, once the author is confident of themodel, the author may then just switch the data stream object for onethat is associated with an infinite data stream, a pseudo-infinite datastream, or a partially pseudo-infinite data stream, without have tochange the model itself.

Regardless of the form of the canonical data 340, the canonical data 340is provided as output data from the data portion 300 and as input datato the analytics portion 400. The canonical data might include fieldsthat include a variety of data types. For instance, the fields mightinclude data types such as integers, floating point numbers, strings,vectors, arrays, collections, hierarchical structures, text, XMLdocuments, tables, lists, SQL database query results, BI (businessintelligence) cube query results, graphical information such as 2Ddrawings and 3D visual models in various formats, or even complexcombinations of these various data types. As another advantage, thecanonicalization process is able to canonicalize a wide variety of inputdata. Furthermore, the variety of input data that the data portion 300is able to accept is expandable. This is helpful in the case wheremultiple models are combined as will be discussed later in thisdescription.

FIG. 4 illustrates analytics portion 400 which represents an example ofthe analytics portion 220 of the pipeline 201 of FIG. 2. The dataportion 300 provided the canonicalized data 401 to the data-modelbinding component 410. The canonicalized data 401 might have anycanonicalized form, and any number of parameters, where the form andnumber of parameters might even differ from one piece of input data toanother. For purposes of discussion, however, the canonical data 401 hasfields 402A through 402H, which may collectively be referred to hereinas “fields 402”.

On the other hand, the analytics portion 400 includes a number of modelparameters 411. The type and number of model parameters may differaccording to the model. However, for purposes of discussion of aparticular example, the model parameters 411 will be discussed asincluding model parameters 411A, 411B, 411C and 411D. In one embodiment,the identity of the model parameters, and the analytical relationshipsbetween the model parameters may be declaratively defined without usingimperative coding.

A data-model binding component 410 intercedes between the canonicalizeddata fields 402 and the model parameters 411 to thereby provide bindingsbetween the fields and parameters. In this case, the data field 402B isbound to model parameter 411A as represented by arrow 403A. In otherwords, the value from data field 402B is used to populate the modelparameter 411A. Also, in this example, the data field 402E is bound tomodel parameter 411B (as represented by arrow 403B), and data field 402His bound to model parameter 411C (as represented by arrow 403C).

The data fields 402A, 402C, 402D, 402F and 402G are not shown bound toany of the model parameters. This is to emphasize that not all of thedata fields from input data are always required to be used as modelparameters. In one embodiment, one or more of these data fields may beused to provide instructions to the data-model binding component 410 onwhich fields from the canonicalized data (for this canonicalized data orperhaps any future similar canonicalized data) are to be bound to whichmodel parameter. This represents an example of the kind of analyticsdata 221 that may be provided to the analytics portion 220 of FIG. 2.The definition of which data fields from the canonicalized data arebound to which model parameters may be formulated in a number of ways.For instance, the bindings may be 1) explicitly set by the author atauthoring time, 2) explicitly set by the user at use time (subject toany restrictions imposed by the author), 3) automatic binding by theauthoring component 240 based on algorithmic heuristics, and/or 4)prompting by the authoring component of the author and/or user tospecify a binding when it is determined that a binding cannot be madealgorithmically. Thus bindings may also be resolved as part of the modellogic itself.

The ability of an author to define which data fields are mapped to whichmodel parameters gives the author great flexibility in being able to usesymbols that the author is comfortable with to define model parameters.For instance, if one of the model parameters represents pressure, theauthor can name that model parameter “Pressure” or “P” or any othersymbol that makes sense to the author. The author can even rename themodel parameter which, in one embodiment, might cause the data modelbinding component 410 to automatically update to allow bindings thatwere previously to the model parameter of the old name to instead bebound to the model parameter of the new name, thereby preserving thedesired bindings. This mechanism for binding also allows binding to bechanged declaratively at runtime.

The model parameter 411D is illustrated with an asterisk to emphasizethat in this example, the model parameter 411D was not assigned a valueby the data-model binding component 410. Accordingly, the modelparameter 411D remains an unknown. In other words, the model parameter411D is not assigned a value.

The modeling component 420 performs a number of functions. First, themodeling component 420 defines analytical relationships 421 between themodel parameters 411. The analytical relationships 421 are categorizedinto three general categories including equations 431, rules 432 andconstraints 433. However, the list of solvers is extensible. In oneembodiment, for example, one or more simulations may be incorporated aspart of the analytical relationships provided a corresponding simulationengine is provided and registered as a solver.

The term “equation” as used herein aligns with the term as it is used inthe field of mathematics.

The term “rules” as used herein means a conditional statement where ifone or more conditions are satisfied (the conditional or “if” portion ofthe conditional statement), then one or more actions are to be taken(the consequence or “then” portion of the conditional statement). A ruleis applied to the model parameters if one or more model parameters areexpressed in the conditional statement, or one or more model parametersare expressed in the consequence statement.

The term “constraint” as used herein means that a restriction is appliedto one or more model parameters. For instance, in a city planning model,a particular house element may be restricted to placement on a maplocation that has a subset of the total possible zoning designations. Abridge element may be restricted to below a certain maximum length, or acertain number of lanes.

An author that is familiar with the model may provide expressions ofthese equations, rules and constraint that apply to that model. In thecase of simulations, the author might provide an appropriate simulationengine that provides the appropriate simulation relationships betweenmodel parameters. The modeling component 420 may provide a mechanism forthe author to provide a natural symbolic expression for equations, rulesand constraints. For example, an author of a thermodynamics-relatedmodel may simply copy and paste equations from a thermodynamicstextbook. The ability to bind model parameters to data fields allows theauthor to use whatever symbols the author is familiar with (such as theexact symbols used in the author's relied-upon textbooks) or the exactsymbols that the author would like to use.

Prior to solving, the modeling component 420 also identifies which ofthe model parameters are to be solved for (i.e., hereinafter, the“output model variable” if singular, or “output model variables” ifplural, or “output model variable(s)” if there could be a single orplural output model variables). The output model variable(s) may beunknown parameters, or they might be known model parameters, where thevalue of the known model parameter is subject to change in the solveoperation. In the example of FIG. 4, after the data-model bindingoperation, model parameters 411A, 411B and 411C are known, and modelparameter 411D is unknown. Accordingly, unknown model parameter 411Dmight be one of the output model variables. Alternatively or inaddition, one or more of the known model parameters 411A, 411B and 411Cmight also be output model variables. The solvers 440 then solve for theoutput model variable(s), if possible. In one embodiment describedhereinafter, the solvers 440 are able to solve for a variety of outputmodel variables, even within a single model so long as sufficient inputmodel variables are provided to allow the solve operation to beperformed. Input model variables might be, for example, known modelparameters whose values are not subject to change during the solveoperation. For instance, in FIG. 4, if the model parameters 411A and411D were input model variables, the solver might instead solve foroutput model variables 411B and 411C instead. In one embodiment, thesolver might output any one of a number of different data types for asingle model parameter. For instance, some equation operations (such asaddition, subtraction, and the like) apply regardless of the whether theoperands are integers, floating point, vectors of the same, or matricesof the same.

In one embodiment, even when the solvers 440 cannot solve for aparticular output model variable, the solver 400 might still present apartial solution for that output model variable, even if a full solve tothe actual numerical result (or whatever the solved-for data type) isnot possible. This allows the pipeline to facilitate incrementaldevelopment by prompting the author as to what information is needed toarrive at a full solve. This also helps to eliminate the distinctionbetween author time and use time, since at least a partial solve isavailable throughout the various authoring stages. For an abstractexample, suppose that the analytics model includes an equation a=b+c+d.Now suppose that a, c and d are output model variables, and b is aninput model variable having a known value of 5 (an integer in thiscase). In the solving process, the solvers 440 are only able to solvefor one of the output model variables “d”, and assign a value of 6 (aninteger) to the model parameter called “d”, but the solvers 440 are notable to solve for “c”. Since “a” depends from “c”, the model parametercalled “a” also remains an unknown and unsolved for. In this case,instead of assigning an integer value to “a”, the solver might do apartial solve and output the string value of “c+11” to the modelparameter “a”. As previously mentioned, this might be especially helpfulwhen a domain expert is authoring an analytics model, and willessentially serve to provide partial information regarding the contentof model parameter “a” and will also serve to cue the author that somefurther model analytics needs to be provided that allow for the “c”model parameter to be solved for. This partial solve result may beperhaps output in some fashion in the view composition to allow thedomain expert to see the partial result.

The solvers 440 are shown in simplified form in FIG. 4. However, thesolvers 440 may direct the operation of multiple constituent solvers aswill be described with respect to FIGS. 10 through 16. In FIG. 4, themodeling component 420 then makes the model parameters 411 (includingthe now known and solved-for output model variables) available as outputto be provided to the view portion 500 of FIG. 5.

FIG. 5 illustrates a view portion 500 which represents an example of theview portion 230 of FIG. 2. The view portion 500 receives the modelparameters 411 from the analytics portion 400 of FIG. 4. The viewportion also includes a view components repository 520 that contains acollection of view components. For example, the view componentsrepository 520 in this example is illustrated as including viewcomponents 521 through 524, although the view components repository 520may contain any number of view components. The view components each mayinclude zero or more input parameters. For example, view component 521does not include any input parameters. However, view component 522includes two input parameters 542A and 542B. View component 523 includesone input parameter 543, and view component 524 includes one inputparameter 544. That said, this is just an example. The input parametersmay, but need not necessarily, affect how the visual item is rendered.The fact that the view component 521 does not include any inputparameters emphasizes that there can be views that are generated withoutreference to any model parameters. Consider a view that comprises justfixed (built-in) data that does not change. Such a view might forexample constitute reference information for the user. Alternatively,consider a view that just provides a way to browse a catalog, so thatitems can be selected from it for import into a model.

Each view component 521 through 524 includes or is associated withcorresponding logic that, when executed by the view compositioncomponent 540 using the corresponding view component input parameter(s),if any, causes a corresponding view item to be placed in virtual space550. That virtual item may be a static image or object, or may be adynamic animated virtual item or object For instance, each of viewcomponents 521 through 524 are associated with corresponding logic 531through 534 that, when executed causes the corresponding virtual item551 through 554, respectively, to be rendered in virtual space 550. Thevirtual items are illustrated as simple shapes. However, the virtualitems may be quite complex in form perhaps even including animation. Inthis description, when a view item is rendered in virtual space, thatmeans that the view composition component has authored sufficientinstructions that, when provided to the rendering engine, the renderingengine is capable of displaying the view item on the display in thedesignated location and in the designated manner.

The view components 521 through 524 may be provided perhaps even as viewdata to the view portion 500 using, for example, the authoring component240 of FIG. 2. The view data may even be a range of a pseudo-infinite orpartially-pseudo infinite data stream provided by a data stream objectas described above with respect to FIG. 6. For instance, the authoringcomponent 240 might provide a selector that enables the author to selectfrom several geometric forms, or perhaps to compose other geometricforms. The author might also specify the types of input parameters foreach view component, whereas some of the input parameters may be defaultinput parameters imposed by the view portion 500. The logic that isassociated with each view component 521 through 524 may be provided withview data, and/or may also include some default functionality providedby the view portion 500 itself.

The view portion 500 includes a model-view binding component 510 that isconfigured to bind at least some of the model parameters tocorresponding input parameters of the view components 521 through 524.For instance, model parameter 411A is bound to the input parameter 542Aof view component 522 as represented by arrow 511A. Model parameter 411Bis bound to the input parameter 542B of view component 522 asrepresented by arrow 511B. Also, model parameter 411D is bound to theinput parameters 543 and 544 of view components 523 and 524,respectively, as represented by arrow 511C. The model parameter 411C isnot shown bound to any corresponding view component parameter,emphasizing that not all model parameters need be used by the viewportion of the pipeline, even if those model parameters were essentialin the analytics portion. Also, the model parameter 411D is shown boundto two different input parameters of view components representing thatthe model parameters may be bound to multiple view component parameters.In one embodiment, the definition of the bindings between the modelparameters and the view component parameters may be formulated by 1)being explicitly set by the author at authoring time, 2) explicitly setby the user at use time (subject to any restrictions imposed by theauthor), 3) automatic binding by the authoring component 240 based onalgorithmic heuristics, and/or 4) prompting by the authoring componentof the author and/or user to specify a binding when it is determinedthat a binding cannot be made algorithmically.

Once again, while not preferred, some or all of the view portion 500 maybe implemented by way of a spreadsheet. For instance, a singlespreadsheet might serve as a foundation for one or more view components,with the respective input parameter(s) of the view componentsrepresented in corresponding spreadsheet cells. The associated viewconstruction logic of the view components may be represented at least inpart using linked expressions within the spreadsheet program. Therendering capabilities of the spreadsheet program, a macro, or someother executable program may then be used to complete the rendering ofthe corresponding visual item. As previously mentioned, however, thisspreadsheet-based implementation is not the preferred embodiment, andthus this description will now turn back to the more general embodimentsof the view portion 500.

As previously mentioned, the view item may include an animation. To takea simple example, consider for example a bar chart that plots acompany's historical and projected revenues, advertising expenses, andprofits by sales region at a given point in time (such as a givencalendar quarter). A bar chart could be drawn for each calendar quarterin a desired time span. Now, imagine that you draw one of these charts,say the one for the earliest time in the time span, and then every halfsecond replace it with the chart for the next time span (e.g., the nextquarter). The result will be to see the bars representing profit, sales,and advertising expense for each region change in height as theanimation proceeds. In this example, the chart for each time period is a“cell” in the animation, where the cell shows an instant betweenmovements, where the collection of cells shown in sequence simulatesmovement. Conventional animation models allow for animation over timeusing built-in hard-coded chart types.

However, using the pipeline 201, by contrast, any kind of visual can beanimated, and the animation can be driven by varying any one or anycombination of the parameters of the visual component. To return to thebar chart example above, imagine that instead of animating by time, weanimate by advertising expense. Each “cell” in this animation is a barchart showing sales and profits over time for a given value ofadvertising expense. Thus, as the advertising expense is varied, thebars grow and shrink in response to the change in advertising expense.

The power of animated data displays is that they make very apparent tothe eye what parameters are most sensitive to change in otherparameters, because you immediately see how quickly and how far eachparameter's values change in response to the varying of the animationparameter.

The pipeline 201 is also distinguished in its ability to animate due tothe following characteristics:

First, the sequences of steps for the animation variable can be computedby the analytics of the model, versus being just a fixed sequence ofsteps over a predefined range. For example, in the example of varyingthe advertising expense as the animation variable, imagine that what isspecified is to “animate by advertising expense where advertisingexpense is increased by 5% for each step” or “where advertising expenseis 10% of total expenses for that step”. A much more sophisticatedexample is “animate by advertising expense where advertising expense isoptimized to maximize the rate of change of sales over time”. In otherwords, the solver will determine a set of steps for advertising spendover time (i.e., for each successive time period such as quarter) suchthat the rate of growth of sales is maximized. Here the user presumablywants to see not only how fast sales can be made to grow by varyingadvertising expense, but also wants to learn the quarterly amounts forthe advertising expenses that achieve this growth (the sequence ofvalues could be plotted as part of the composite visual).

Second, any kind of visual can be animated, not just traditional datacharts. For example, consider a Computer-Aided Design (CAD) model of ajet engine that is a) to be animated by the air speed parameter and 2)where the rotational speed of the turbine is a function of the air speedand 3) where the temperature of the turbine bearings is a function ofthe air speed. Jet engines have limits on how fast turbines can berotated before either the turbine blades lose integrity or the bearingoverheats. Thus, in this animation we desire that as air speed is variedthe color of the turbine blades and bearing should be varied from blue(safe) to red (critical). The values for “safe” and “critical” turbineRPM and bearing temperature may well be calculated by the model based onphysical characteristics of those parts. Now, as the animation variesthe air speed over a defined range, we see the turbine blades andbearing each change color. What is now interesting is to notice whichreaches critical first, and if either undergoes a sudden (runaway) runto critical. These kinds of effects are hard to discern by looking at achart or at a sequence of drawings, but become immediately apparent inan animation. This is but one example of animating an arbitrary visual(CAD model) by an arbitrary parameter (air speed), with the animationaffecting yet other arbitrary parameters (turbine RPM and bearing temp).Any parameter(s) of any visual(s) can be animated according to anydesired parameter(s) that are to serve as the animation variables.

Third, the pipeline 201 can be stopped mid stream so that data andparameters may be modified by the user, and the animation then restartedor resumed. Thus, for example, in the jet engine example, if runawayheating is seen to start at a given air speed, the user may stop theanimation at the point the runaway beings, modify some engine designcriterion, such as the kind of bearing or bearing surface material, andthen continue the animation to see the effect of the change.

As with other of the capabilities discussed herein, animations can bedefined by the author, and/or left open for the user to manipulate totest various scenarios. For example, the model may be authored to permitsome visuals to be animated by the user according to parameters the userhimself selects, and/or over data ranges for the animation variable thatthe user selects (including the ability to specify computed rangesshould that be desired). Such animations can also be displayed side byside as in the other what-if comparison displays. For example, a usercould compare an animation of sales and profits over time, animated bytime, in two scenarios with differing prevailing interest rates in thefuture, or different advertising expenses ramps. In the jet engineexample, the user could compare the animations of the engine for boththe before and after cases of changing the bearing design.

At this point, a specific example of how the composition framework maybe used to actually construct a view composition will be described withrespect to FIG. 7, which illustrated 3-D renderings 700 of a viewcomposition that includes a room layout 701 with furniture laid outwithin the room, and also includes a Feng Shui meter 702. This exampleis provided merely to show how the principles described herein can applyto any arbitrary view composition, regardless of the domain.Accordingly, the example of FIG. 7, and any other example viewcomposition described herein, should be viewed strictly as only anexample that allows the abstract concept to be more fully understood byreference to non-limiting concrete examples, and not defining thebroader scope of the invention. The principles described herein mayapply to construct a countless variety of view compositions.Nevertheless, reference to a concrete example can clarify the broaderabstract principles.

FIG. 8 illustrates a flowchart of a method 800 for generating a viewconstruction. The method 800 may be performed by the pipelineenvironment 200 of FIG. 2, and thus will be described with frequentreference to the pipeline environment 200 of FIG. 2, as well as withreference to FIGS. 3 through 5, which each show specific portions of thepipeline of FIG. 2. While the method 800 may be performed to constructany view composition, the method 800 will be described with respect tothe view composition 700 of FIG. 7. Some of the acts of the method 800may be performed by the data portion 210 of FIG. 2 and are listed in theleft column of FIG. 8 under the header “Data”. Other of the acts of themethod 800 may be performed by the analytics portion 220 of FIG. 2, andare listed in the second from the left column of FIG. 8 under the header“Analytics”. Other of the acts of the method may be performed by theview portion 230 of FIG. 2, and are listed in the second from the rightcolumn of FIG. 8 under the header “View”. One of the acts may beperformed by a rendering module and is listed in the right column ofFIG. 8 under the header other.

Referring to FIG. 8, the data portion accesses input data that at leastcollectively affects what visual items are displayed or how a given oneor more of the visual items are displayed (act 811). For instance,referring to FIG. 7, the input data might include view components foreach of the items of furniture. For instance, each of the couch, thechair, the plants, the table, the flowers, and even the room itself maybe represented by a corresponding view component. The view componentmight have input parameters that are suitable for the view component. Ifanimation were employed, for example, some of the input parameters mightaffect the flow of the animation. Some of the parameters might affectthe display of the visual item, and some parameters might not.

For instance, the room itself might be a view component. Some of theinput parameters might include the dimensions of the room, theorientation of the room, the wall color, the wall texture, the floorcolor, the floor type, the floor texture, the position and power of thelight sources in the room, and so forth. There might also be roomparameters that do not necessarily get reflected in this viewcomposition, but might get reflected in other views and uses of the roomcomponent. For instance, the room parameter might have a location of theroom expressed in degrees, minutes, and seconds longitude and latitude.The room parameter might also include an identification of the author ofthe room component, and the average rental costs of the room.

The various components within the room may also be represented by acorresponding parameterized view component. For instance, each plant maybe configured with an input parameter specifying a pot style, a potcolor, pot dimensions, plant color, plant resiliency, plant dependencieson sunlight, plant daily water intake, plant daily oxygen production,plant position and the like. Once again, some of these parameters mayaffect how the display is rendered and others might not, depending onthe nature of what is being displayed.

The Feng Shui meter 702 may also be a view component. The meter mightinclude input parameters such as a diameter, a number of wedges to becontained in the diameter of the meter, a text color and the like. Thevarious wedges of the Feng Shui meter may also be view components. Inthat case, the input parameters to the view components might be a title(e.g., water, mountain, thunder, wind, fire, earth, lake, heaven),perhaps a graphic to appear in the wedge, a color hue, or the like.

The analytics portion binds the input data to the model parameters (act821), determines the output model variables (act 822), and uses themodel-specific analytical relationships between the model parameters tosolve for the output model variables (act 823). The binding operation ofact 821 has been discussed above, and essentially allows flexibility inallowing the author to define the model analytics equations, rules andconstraints using symbols that the model author is comfortable with. Themore complex solver described with respect to FIGS. 10 through 16 mayserve to solve for the output model variables (act 823).

The identification of the output model variables may differ from onesolving operation to the next. Even though the model parameters may staythe same, the identification of which model parameters are output modelvariables will depend on the availability of data to bind to particularmodel parameters. This has remarkable implications in terms of allowinga user to perform what-if scenarios in a given view composition.

For instance, in the Feng Shui room example of FIG. 7, suppose the userhas bought a new chair to place in their living room. The user mightprovide the design of the room as data into the pipeline. This might befacilitated by the authoring component prompting the user to enter theroom dimensions, and perhaps provide a selection tool that allows theuser to select virtual furniture to drag and drop into the virtual roomat appropriate locations that the actual furniture is placed in theactual room. The user might then select a piece of furniture that may beedited to have the characteristics of the new chair purchased by theuser. The user might then drag and drop that chair into the room. TheFeng Shui meter 702 would update automatically. In this case, theposition and other attributes of the chair would be input modelvariables, and the Feng Shui scores would be output model variables. Asthe user drags the virtual chair to various positions, the Feng Shuiscores of the Feng Shui meter would update, and the user could thus testthe Feng Shui consequences of placing the virtual chair in variouslocations. To avoid the user from having to drag the chair to everypossible location to see which gives the best Feng Shui, the user canget local visual clues (such as, for example, gradient lines or arrows)that tell the user whether moving the chair in a particular directionfrom its current location makes things better or worse, and how muchbetter or worse.

However, the user could also do something else that is unheard of inconventional view composition. The user could actually change the outputmodel variables. For instance, the user might indicate the desired FengShui score in the Feng Shui meter, and leave the position of the virtualchair as the output model variable. The solver would then solve for theoutput model variable and provide a suggested position or positions ofthe chair that would achieve at least the designated Feng Shui score.The user may choose to make multiple parameters output model variables,and the system may provide multiple solutions to the output modelvariables. This is facilitated by a complex solver that is described infurther detail with respect to FIGS. 10 through 16.

Returning to FIG. 8, once the output model variables are solved for, themodel parameters are bound to the input parameters of the parameterizedview components (act 831). For instance, in the Feng Shui example, afterthe unknown Feng Shui scores are solved for, the scores are bound asinput parameters to Feng Shui meter view component, or perhaps to theappropriate wedge contained in the meter. Alternatively, if the FengShui scores were input model variables, the position of the virtualchair may be solved for and provided as an input parameter to the chairview component.

A simplified example will now be presented that illustrates theprinciples of how the solver can rearrange equations and change thedesignation of input and output model variables all driven off of oneanalytical model. The user herself does not have to rearrange theequations. The simplified example may not accurately represent Feng Shuirules, but illustrates the principle nevertheless. Suppose the totalFeng Shui (FS) of the room (FSroom) equals the FS of a chair (FSchair)and the FS of a plant (FSplant). Suppose FSchair is equal to a constantA times the distance d of the chair from the wall. Suppose FSplant is aconstant, B. The total FS of the room is then: FSroom=A*d+B. If d is aninput model variable, then FSroom is an output model variable and itsvalue, displayed on the meter, changes as the user repositions thechair. Now suppose the user clicks on the meter making it an input modelvariable and shifting d into unknown output model variable status. Inthis case, the solver effectively and internally rewrites the equationabove as d=(FSroom−B)/A. As mentioned in other portions of thisdescription in further detail, some equations can be rewritten in theway illustrated here. Other equations cannot. When an equation cannot beso rewritten, approximation or numerical techniques may be used likeNewton-Raphson hill climbing algorithms that allow some inversion of theequation set. Basically, Newton Raphson would allow the system to samplethe value of a function while changing one of the variables, and thusfind a solution for that variable without inverting by rewriting.Accordingly, the principles described herein are not limited toanalytics that can be solved directly or by rewriting the analytics. Inthe example just provided, however, the view component can move thechair around, changing d, its distance from the wall, as the userchanges the desired value, FSroom, on the meter.

The view portion then constructs a view of the visual items (act 832) byexecuting the construction logic associated with the view componentusing the input parameter(s), if any, to perhaps drive the constructionof the view item in the view composition. The view construction may thenbe provided to a rendering module, which then uses the view constructionas rendering instructions (act 841).

In one embodiment, the process of constructing a view is treated as adata transformation that is performed by the solver. That is, for agiven kind of view (e.g., consider a bar chart), there is a modelconsisting of rules, equations, and constraints that generates the viewby transforming the input data into a displayable output data structure(called a scene graph) which encodes all the low level geometry andassociated attributes needed by the rendering software to drive thegraphics hardware. In the bar chart example, the input data would be forexample the data series that is to be plotted, along with attributes forthings like the chart title, axis labels, and so on. The model thatgenerates the bar would have rules, equations, and constraints thatwould do things like 1) count how many entries the data series consistsof in order to determine how many bars to draw, 2) calculate the range(min, max) that the data series spans in order to calculate things likethe scale and starting/ending values for each axis, 3) calculate theheight of the bar for each data point in the data series based on thepreviously calculated scale factor, 4) count how many characters are inthe chart title in order to calculate a starting position and size forthe title so that the title will be properly located and centered withrespect to the chart, and so forth. In sum, the model is designed tocalculate a set of geometric shapes based on the input data, with thosegeometric shapes arranged within a hierarchical data structure of type“scene graph”. In other words, the scene graph is an output variablethat the model solves for based on the input data. Thus, an author candesign entirely new kinds of views, customize existing views, andcompose preexisting views into composites, using the same framework thatthe author uses to author, customize, and compose any kind of model.Thus, authors who are not programmers can create new views withoutdrafting new code.

Returning to FIG. 2, recall that the user interaction response module250 detects when the user interacts with the view composition, andcauses the pipeline to respond appropriately. FIG. 9 illustrates aflowchart of a method 900 for responding to user interaction with theview composition. In particular, the user interaction response moduledetermines which the components of the pipeline should perform furtherwork in order to regenerate the view, and also provides data representedthe user interaction, or that is at least dependent on the userinteraction, to the pipeline components. In one embodiment, this is donevia a transformation pipeline that runs in the reverse (upstream)view/analytics/data direction and is parallel to the (downstream)data/analytics/view pipeline.

Although the broader principles of the present invention are not limitedto the type of interaction or how interactions are implemented,interactions can be posted as events into the upstream pipeline. Forinstance, each transformer in the data/analytics/view pipeline providesan upstream transformer that handles incoming interaction data. Thesetransformers can either be null (passthroughs, which get optimized outof the path) or they can perform a transformation operation on theinteraction data to be fed further upstream. This provides positiveperformance and responsiveness of the pipeline in that 1) interactionbehaviors that would have no effect on upstream transformations, such asa view manipulation that has no effect on source data, can be handled atthe most appropriate (least upstream) point in the pipeline and 2)intermediate transformers can optimize view update performance bysending heuristically-determined updates back downstream, ahead of thefinal updates that will eventually come from further upstreamtransformers. For example, upon receipt of a data edit interaction, aview-level transformer could make an immediate view update directly intothe scene graph for the view (for edits it knows how to interpret), withthe final complete update coming later from the upstream datatransformer where the source data is actually edited.

When the semantics of a given view interaction have a nontrivial mappingto the needed underlying data edits, intermediate transformers canprovide the needed upstream mapping. For example, dragging a point on agraph of a computed result could require a backwards solve that wouldcalculate new values for multiple source data items that feed thecomputed value on the graph. The solver-level upstream transformer wouldbe able to invoke the needed solve and to propagate upstream the neededdata edits.

FIG. 9 illustrates a flowchart of a method 900 for responding to userinteraction with the view construction. Upon detecting that the user hasinteracted with the rendering of a view composition on the display (act901), it is first determined whether or not the user interactionrequires regeneration of the view (decision block 902). This may beperformed by the rendering engine raising an event that is interpretedby the user interaction response component 250 of FIG. 2. If the userinteraction does not require regeneration of the view (No in decisionblock 902), then the pipeline does not perform any further action toreconstruct the view (act 903), although the rendering engine itself mayperform some transformation on the view. An example of such a userinteraction might be if the user were to increase the contrast of therendering of the view construction, or rotate the view construction.Since those actions might be undertaken by the rendering engine itself,the pipeline need perform no work to reconstruct the view in response tothe user interaction.

If, on the other hand, it is determined that the type of userinteraction does require regeneration of the view construction (Yes indecision block 902), the view is reconstructed by the pipeline (act904). Either the whole view construction or a part of the viewconstruction may be regenerated. By optionally keeping track ofdependencies, it can be inferred which view elements or interactionsdepend on which data or analytics. In that case, regeneration may beachieved by regenerating only those elements that depend on theinteraction. More regarding how these dependencies are inferred will bedescribed further below. Interaction may result in some altering of thedata provided to the pipeline. For instance, in the Feng Shui example,suppose the user was to move the position of the virtual chair withinthe virtual room, the position parameter of the virtual chair componentwould thus change. An event would be fired informing the analyticsportion that the corresponding model parameter representing the positionof the virtual chair should be altered as well. The analytics componentwould then resolve for the Feng Shui scores, repopulate thecorresponding input parameters of the Feng Shui meter or wedges, causingthe Feng Shui meter to update with current Feng Shui scores suitable forthe new position of the chair.

The user interaction might require that model parameters that werepreviously known are now unknown, and that previously unknown parametersare now known. That is one of several possible examples that mightrequire a change in designation of input and output model variables suchthat previously designated input model variables might become outputmodel variables, and vice versa. In that case, the analytics portionwould solve for the new output model variable(s) thereby driving thereconstruction of the view composition.

This type of reconstruction is helpful in constructing alternative viewsof a view composition that is driven by different data. However, this isalso helpful in storytelling, by causing transitions in a view to occurfrom one view composition to the next. Traditionally, this story tellinghas been done by taking snapshots of multiple different configurationsof data within visualizations (which means the visualizations are alsodifferent), and then turning these snapshots into slides with simpletransitions between the slides.

However, this approach does not work well more sophisticated visuals,such as perhaps the visualization of a room in the Feng Shui example, orperhaps a visualization of a complex piece of machinery such as atractor. Unlike the simple visualizations used in charts, in real worldor other sophisticated visualizations, there are a lot of permutationsof what can be changed. Moreover, there is a continuum within possiblechanges to the visuals (e.g. a tractor arm can move to multiplepositions continuously, and that arm move could lead to repositioning(possibly continuously) of many other elements. Or perhaps, any numberof furniture or other room elements may be moved around and changed out.

Another approach that is taken is scripting of visualizations into astoryboard. The trouble here is that it is hard to make the script datadriven, and it is hard for the script to presage and allow for the verymany changes of data and visuals that a user could do, and theconsequent propagated changes to the visuals in a way that respectsconstraints and other relationships within the data and visuals.

When constructed using the pipeline 201 described herein, on the otherhand, very complex view compositions may be constructed. Each viewcomposition might be a scene in a storyboard of complex scenes. Thestoryboard may transition from one scene to another by altering the viewcomposition in one of a variety of possible ways. For instance, atransition might involve one some or all of the following transitions:

1) A visuals transformation: In this transformation, the data drivingthe view composition may stay the same, but the set of view componentsused to construct the view composition may change. In other words, theview on the data changes. For instance, a set of data might includeenvironmental data including temperature, wind speed, and other datarepresenting a sequence of environmental activities, such as a lightningstrike. One scene might manifest that data in the context of an aircraftflying through that environment. The next scene in the storyboard mighttell of another aircraft that is subjected to the entirely sameenvironmental conditions.

2) Data transformation: Here, the view components stay the same. Inother words, the view is kept the same, but the data that affects visualproperties changes, thereby changing how the view is rendered. Forinstance, the data may change, and/or the binding of the data to modelparameters changes.

3) Coordinate system transformation. Here, the data and set of viewcomponents may stay the same, but the coordinated system is changed fromone coordinate system to another.

4) Target world transformation. Here, everything may stay the same, butthe target virtual world changes. For example, one geometry may becomesuperimposed upon another. An example of geometries, and superimposedgeometries will be provided hereinafter.

Solver Framework

FIG. 10 illustrates a solver environment 1000 that may represent anexample of the solvers 440 of FIG. 4. The solver environment 1000 may beimplemented in software, hardware, or a combination. The solverenvironment 1000 includes a solver framework 1001 that manages andcoordinates the operations of a collection 1010 of specialized solvers.The collection 1010 is illustrated as including three specializedsolvers 1011, 1012 and 1013, but the ellipsis 1014 represents that therecould be other numbers (i.e., more than three or less than three) ofspecialized solvers as well. Furthermore, the ellipsis 1014 alsorepresents that the collection 1010 of specialized solvers isextensible. As new specialized solvers are discovered and/or developedthat can help with the model analytics, those new specialized solversmay be incorporated into the collection 1010 to supplement the existingspecialized solvers, or perhaps to replace one or more of the existingsolvers. For example, FIG. 10 illustrates that a new solver 1015 isbeing registered into the collection 1010 using the solver registrationmodule 1021. As one example, a new solver might be perhaps a simulationsolver which accepts one or more known values, and solves for one ormore unknown values. Other examples include solvers for systems oflinear equations, differential equations, polynomials, integrals,root-finders, factorizers, optimizers, and so forth. Every solver canwork in numerical mode or in symbolic mode or in mixed numeric-symbolicmode. The numeric portions of solutions can drive the parameterizedrendering downstream. The symbolic portions of the solution can drivepartial solution rendering.

The collection of specialized solvers may include any solver that issuitable for solving for the output model variables. If, for example,the model is to determine drag of a bicycle, the solving of complexcalculus equations might be warranted. In that case, a specializedcomplex calculus solver may be incorporated into the collection 1010 toperhaps supplement or replace an existing equations solver. In oneembodiment, each solver is designed to solve for one or more outputmodel variables in a particular kind of analytics relationship. Forexample, there might be one or more equation solvers configured to solvefor unknowns in an equation. There might be one or more rules solversconfigured to apply rules to solve for unknowns. There might be one ormore constraints solvers configured to apply constraints to therebysolve for unknowns. Other types of solves might be, for example, asimulation solver which performs simulations using input data to therebyconstruct corresponding output data.

The solver framework 1001 is configured to coordinate processing of oneor more or all of the specialized solvers in the collection 1010 tothereby cause one or more output model variables to be solved for. Thesolver framework 1001 is then configured to provide the solved-forvalues to one or more other external components. For instance, referringto FIG. 2, the solver framework 1001 may provide the model parametervalues to the view portion 230 of the pipeline, so that the solvingoperation thereby affects how the view components execute to render aview item, or thereby affect other data that is associated with the viewitem. As another potential effect of solving, the model analyticsthemselves might be altered. For instance, as just one of many examplesin which this might be implemented, the model might be authored withmodifiable rules set so that, during a given solve, some rule(s) and/orconstraint(s) that are initially inactive become activated, and somethat are initially activated become inactivated. Equations can bemodified this way as well.

FIG. 11 illustrates a flowchart of a method 1100 for the solverframework 1001 to coordinate processing amongst the specialized solversin the collection 1010. The method 1100 of FIG. 11 will now be describedwith frequent reference to the solver environment 1000 of FIG. 10.

The solver framework begins a solve operation by identifying which ofthe model parameters are input model variables (act 1101), and which ofthe model parameters are output model variables (act 1102), and byidentifying the model analytics that define the relationship between themodel parameters (act 1103). Given this information, the solverframework analyzes dependencies in the model parameters (act 1104). Evengiven a fixed set of model parameters, and given a fixed set of modelanalytics, the dependencies may change depending on which of the modelparameters are input model variables and which are output modelvariables. Accordingly, the system can infer a dependency graph eachtime a solve operation is performed using the identity of which modelparameters are input, and based on the model analytics. The user neednot specify the dependency graph for each solve. By evaluatingdependencies for every solve operation, the solver framework has theflexibility to solve for one set of one or more model variables duringone solve operation, and solve for another set of one or more modelvariables for the next solve operation. In the context of FIGS. 2through 5, that means greater flexibility for a user to specify what isinput and what is output by interfacing with the view composition.

FIG. 43 illustrates a flowchart of a method 4300 for inferring adependency graph. The method 4300 uses the output model parameter(s) andthe available model analytics to identify one of more analytics (act4301) (i.e., equations, rules, constraints, simulations, etc) that maybe used to solve for at least one of the output model parameters. Forexample, suppose that “A1” and “A2” are output model parameters for aparticular solve. The solver might have access to hundreds of analyticsexpressions. However, perhaps only a few of those analytics expressionswould be helpful to obtain values for “A1” and “A2”. For example, theremight be an expression A1^(B1)=C1. In that case, values for B1 and C1may be used with an inverted version of this equation to obtain a valuefor A1. There might also be another expression B2+C2=A2. In that case,values for B2 and C2 may be used with this equation to obtain a valuefor A2, even without any inversion of the equation.

Thus, a portion of the dependency graph has been constructed based onthe analytic(s) identified (act 4302). If one or more of the parametersB1, C1, B2 and C2 are not input parameters, however, then the process ofconstructing the dependency graph is not yet complete. Specifically, forthose parameters that are not input parameters, and are not outputparameters, the parameter may be identified as an intermediate outputparameter (act 4303) needed to be solved for in order to arrive at theultimate output parameter. These intermediate parameters may be treatedas an output parameter for purposes of a next level of recursion. Forinstance, in the above example, parameters B1, C1, B2 and C2 might notbe input parameters, but are instead intermediate parameters that are tobe solved for in order to eventual arrive at a value of outputparameters A1 and A2. Once a level of recursion is encountered in whichall of the parameters needed to solve for an intermediate parameter areinput parameter(s), then the level of recursion may reverse back to alevel at which not all of the parameters needed to solve for anintermediate parameter are input parameter(s). The end result will be aviable dependency graph.

It may be that this recursive process may not result in a viabledependency graph. For instance, perhaps there is no way to use thedependency graph and the input model parameter(s) to solve for theoutput model parameter(s). In that case, the process may be performedagain, but for a different possible construction of the dependencygraph. For instance, there might be multiple analytics that mightindependently be used to solve for a particular output or intermediateparameter. In that case, choosing one of the analytics might result in adead-end as far as constructing the dependency graph with no viabledependency connection between the input parameter(s) and the outputparameter(s), whereas the choice of another analytics might result in asuccessful dependency graph with viable connections between the inputparameter(s) and the output parameter(s). For instance, perhaps if theequation A1^(B1)=C1 is used to solve for A1, then there is no way tosolve for B1 or C1, and thus no way to solve for A1. However, theremight be an alternative equation D1−B1=A1. Perhaps it is possible tosolve for D1 and B1 using the remainder of the available analytics.Thus, the system can infer a dependency graph using the equationD1−B1=A1. As the analytics change, or as the input or output modelparameter(s) change, the dependency graph may be recalculated.

The analytics might change if the set of equations, rules, constraints,simulations, or the like, changes. The analytics might change if theidentity of the input parameter(s) and/or output parameter(s) changes.The analytics might also change if the value(s) of one or moreparameter(s) change. Some of these analytics changes might require anentire resolve of the analytics. For instance, a new dependency graphmight be inferred, with a complete resolve of all analytics that leadthrough the dependency graph from the input model parameter(s) to theoutput model parameter(s). However, some analytics changes might notrequire a complete resolve. For instance, even if the dependency graphwere to change, there might be some analytic expressions that remain thesame as far as the identity of the known parameter(s) of that analyticexpression, and the identity of the unknown parameter(s) of thatanalytic expression. In that case, if the expression remains to besolved, there is no need to redo the solve of the expression. Theresults from the prior solve may be reliably used in that case. The netimpact of this is that a resolve might not require a solve of all of theanalytic expressions that lead from the input model parameter(s) to theoutput model parameter(s).

Each node in the dependency graph represents a value of an intermediateparameter that is solved for on the way to solving for one or moreoutput model parameter(s). Each node may have input links to othernodes, where those nodes represent other parameter(s) (e.g., anotherintermediate parameter, or an input parameter) which, when used with anidentified analytical expression, leads to the subject intermediateparameter. Each intermediate parameter may also have output links toother nodes, where those nodes represent other parameter(s) (e.g.,another intermediate parameter, or an output model parameter) that maybe solved for using at least the intermediate parameter given anidentified analytical expression. Multiple input links may lead into anode if multiple parameters are used with an analytical expression todetermine the corresponding intermediate parameter.

The dependency tree may actually be visually evaluated by the user. Forinstance, the user may identify the parameter (e.g., an input modelparameter, and output model parameter, or even an intermediateparameter) and/or the value of the parameter, for at least some, andpotentially all of the nodes in the dependency graph. Furthermore, theuser may be given a visual indication of the analytical expressions thatare used to link the nodes in the dependency tree.

The user may be given a user interface that allows the user to view andperhaps edit properties associated with a particular node in thedependency tree. For instance, the user might edit the value or identityof a parameters represented by the node in that dependency tree. Theuser might also be provided with a user interface that allows the userto view and perhaps edit properties associated with a link in thedependency graph. For instance, the user might view the analyticalexpression (or perhaps even edit the analytical expression) that linksnodes in the dependency graph. Such editing may perhaps result in someautomated reconfiguring of the dependency graph, and may perhaps changethe structure of the dependency graph.

In some solve operations, the model may not have any output modelvariables at all. In that case, the solve will verify that all of theknown model parameter values, taken together, satisfy all therelationships expressed by the analytics for that model. In other words,if you were to erase any one data value, turning it into an unknown, andthen solve, the value that was erased would be recomputed by the modeland would be the same as it was before. Thus, a model that is loaded canalready exist in solved form, and of course a model that has unknownsand gets solves now also exists in solved form. What is significant isthat a user interacting with a view of a solved model is neverthelessable to edit the view, which may have the effect of changing a datavalue or values, and thus cause a re-solve that will attempt torecompute data values for output model variables so that the new set ofdata values is consistent with the analytics. Which data values a usercan edit (whether or not a model starts with output model variables) iscontrolled by the author; in fact, this is controlled by the authordefining which variables represented permitted unknowns.

If there are expressions that have one or more unknowns that may beindependently solved without first solving for other unknowns in otherexpressions (Yes in decision block 1105), then those expressions may besolved at any time (act 1106), even perhaps in parallel with othersolving steps. On the other hand, if there are expressions that haveunknowns that cannot be solved without first solving for an unknown inanother expression, then a solve dependency has been found. In thatcase, the expression becomes part of a relational structure (such as adependency tree) that defines a specific order of operation with respectto another expression.

In the case of expressions that have interconnected solve dependenciesfrom other expressions, an order of execution of the specialized solversis determined based on the analyzed dependencies (act 1107). The solversare then executed in the determined order (act 1108). In one example, inthe case where the model analytics are expressed as equations,constraints, and rules, the order of execution may be as follows 1)equations with dependencies or that are not fully solvable as anindependent expression are rewritten as constraints 2) the constraintsare solved, 3) the equations are solved, and 4) the rules are solved.The rules solving may cause the data to be updated.

Once the solvers are executed in the designated order, it is thendetermined whether or not solving should stop (decision block 1109). Thesolving process should stop if, for example, all of the output modelvariables are solved for, or if it is determined that even though notall of the output model variables are solved for, the specializedsolvers can do nothing further to solve for any more of the output modelvariables. If the solving process should not end (No in decision block1109), the process returns back to the analyzing of dependencies (act1104). This time, however, the identity of the input and output modelvariables may have changed due to one or more output model variablesbeing solved for. On the other hand, if the solving process should end(Yes in decision block 1109) the solve ends (act 1110). However, if amodel cannot be fully solved because there are too many output modelvariables, the model nevertheless may succeed in generating a partialsolution where the output model variables have been assigned symbolicvalues reflective of how far the solve was able to proceed. For example,if a model has an equation A=B+C, and B is known to be “2” and is aninput model variable but C is an output model variable and A is also anoutput model variable and needs to be solved for, the model solvercannot product a numerical value for A since while B is known C isunknown; so instead of a full solve, the solver returns “2+C” as thevalue for A. It is thus clear to the author what additional variableneeds to become known, either by supplying it a value or by addingfurther rules/equations/constraints or simulations that can successfullyproduce the needed value from other input data.

This method 1100 may repeat each time the solver framework detects thatthere has been a change in the value of any of the known modelparameters, and/or each time the solver framework determines that theidentity of the known and unknown model parameters has changed. Solvingcan proceed in at least two ways. First, if a model can be fully solvedsymbolically (that is, if all equations, rules, and constraints can bealgorithmically rewritten so that a computable expression exists foreach unknown) then that is done, and then the model is computed. Inother words, data values are generated for each unknown, and/or datavalues that are permitted to be adjusted are adjusted. As a secondpossible way, if a model cannot be fully solved symbolically, it ispartially solved symbolically, and then it is determined if one or morenumerical methods can be used to effect the needed solution. Further, anoptimization step occurs such that even in the first case, it isdetermined whether use of numerical methods may be the faster way tocompute the needed values versus performing the symbolic solve method.Although the symbolic method can be faster, there are cases where asymbolic solve may perform so many term rewrites and/or so manyrewriting rules searches that it would be faster to abandon this andsolve using numeric methods.

FIG. 12 illustrates a solver environment 1200 that represents an exampleof the solver environment 1000 of FIG. 10. In this case, the solvercoordination module 1210 acts to receive the input model variables 1201and coordinate the actions of the forward solver 1221, the symbolicsolver 1222 (or the “inverter”), and the numeric solver 1223 such thatthe model variables 1202 (including the output model variables) aregenerated. The forward solver 1221, the symbolic solver 1222 and thenumeric solver 1223 are examples of the solvers that might be in thesolver collection 1010 of FIG. 10.

The solver coordination module 1210 maintains a dependency graph of themodel analytics that have corresponding model variables. For each solveoperation, the solver coordination module 1210 may determine which ofthe model variables are input model variables, and which of the modelvariables are output model variables and thus are to be solved for.

The forward solver 1221 solves model analytics that are properlypresented so as to be forward solvable. For instance, if there is butone equation in the model analytics A=B+C, and if B and C are inputmodel variables, then A can be solved for using a forward solve byplugging in the values for B and C into the equation, and determiningthe resulting value for A.

The symbolic solver 1222 rewrites model analytics so as to be forwardsolvable. For instance, suppose in the equation A=B+C, it is variables Aand C that are input variables, and variable B that is an outputvariable to be solved for. In this situation, the model analytics cannotbe forward solved without first inverting the model analytics (in thiscase inverting the equation). Accordingly, the symbolic solver 1222rewrites the equation A=B+C as B=A−C. Now, the inverted equation can besubjected to a forward solve by the forward solver 1221 such that theinput variables A and C are plugged into the equation B=A−C to obtainthe value of variable B.

Some equations are not mathematically invertible, or at least it has notyet been discovered how to invert some types of equations. Furthermore,even if the equation is invertible, or it is known how to invert theequation, the symbolic solver 1222 might simply not be able to invertthe equation. Or perhaps it is simply inefficient for the symbolicsolver 1222 to invert the equation as compared to resorting to othersolving methods, such as numeric solving. Accordingly, the numericsolver 1223 is provided to solve model analytics using model analyticsin the case where the model analytics are not properly invertible(either because inversion was not possible, not known, or not enabled bythe symbolic solver).

The solver coordination module 1210 is configured to manage each solveoperation. For instance, FIG. 13 illustrates a flowchart of a method1300 for managing the solve operation such that model analytics may besolved for. The method 1300 may be managed by the solver environment1200 under the direction of the solver coordination module 1210.

The solver coordination module 1210 identifies which of the modelvariables of the model analytics are input variable(s) for a particularsolve, and which of the model variables are output model variable(s) fora particular solve (act 1301). If, for example, the input and outputmodel variables are defined in FIG. 4 by the data-model binder component410, even given a constant set of model variables, the identity of theinput model variables and the output model variables may change from onesolve operation to the next. Accordingly, the coordination of the solveoperation may change from one solve operation to the next. For example,even given a constant set of model analytics, depending on the inputmodel variables, a forward solve may be sufficient for one solveoperation, an inversion and a forward solve of the inverted analyticsmay be sufficient for another solve operation, and perhaps a numericsolve may be sufficient for yet another solve operation.

However, if implemented in the context of the analytics portion 220 ofthe pipeline 201, even the model analytics may change as the modelanalytics are formulated or perhaps combined with other model analyticsas previously described. The solver environment 1200 may account forthese changes by identifying the input and output model variableswhenever there is a change, by accounting for any changed modelanalytics, and solving appropriately.

For each solve, once the input and output model variables are identified(act 1301), the solver coordination module 1210 determines whether ornot a forward solve of the output parameter(s) is to be performed giventhe input model variables (s) without first inverting the modelanalytics (decision block 1302). If a forward solve is to be performed(Yes in decision block 1302), the forward solver 1221 is made to forwardsolve the model analytics (act 1303). This forward solve may be of theentire model analytics, or of only a portion of the model analytics. Inthe latter case, the method 1300 may be executed once again, only thistime with a more complete set of input model variables that include themodel variables solved for in the forward solve.

If it is determined that the forward solve of the output parameter(s) isnot to be performed for the particular solve at least not without firstinverting the model analytics (No in decision block 1302), it is thendetermined whether or not the model analytics is to be inverted for theparticular solve such that a forward solve may solve for the outputparameter(s) (decision block 1304). If the model analytics (or at leasta portion of the model analytics) is to be inverted (Yes in decisionblock 1304), the model analytics is inverted by the symbolic solver (act1305). Thereafter, the inverted model analytics may be solved for usinga forward solve (act 1303). Once again, if only a portion of the modelanalytics was solved for in this way, the method 1300 may be executedagain, but with an expanded set of input model variables.

If it is determined that the model analytics are not to be inverted forthe particular solve (No in decision block 1304), then the numericsolver may solve for the output variable(s) using numeric methods (act1306). Once again, if only a portion of the model analytics was solvedfor in this way, the method 1300 may be executed again, but with anexpanded set of input model variables.

Accordingly, a flexible solver environment 1300 has been described inwhich a wide variety of model analytics may be solved for regardless ofwhich model variables are input and which model variables are outputfrom one solve operation to the next.

FIG. 14 illustrates a flowchart of another method 1400 that may beperformed by the solver framework 1001 shown in FIG. 10. In particular,a solver solves for a model variable (act 1401), which may define aproperty of a view component of a view composition. For example, thesolver may use a known model variable to solve for an unknown modelvariable. In some instances, the known model variable may be an outputprovided by another solver, such as when the solvers form part of arelational structure, such as a dependency tree, mentioned above withrespect to FIGS. 10 and 11.

The solve operation causes an actual change in the canonical data (act1402). After the solver solves for the model variable, a property of aview component of the view composition is then set to the value of thesolved model variable (act 1403). For example, the solved model variablemay be provided as part of the model parameters 411 shown in FIG. 5,which may be bound to an input parameter 542 of a first view component520. In some instances, a known model variable that was used to solvefor the solved model variable may define another property of the firstview component 520. In such instances, the known model variable andsolved model variable may be bound to various input parameters 542 ofthe first view component 520. In other instances, the known modelvariable may define a property of a second view component 520, such aswhen the first view component is a child or a parent of the second viewcomponent. In these other instances, the solved model variable may bebound to an input parameter 542 of the first view component 520, whilethe known model variable may be bound to an input parameter 542 of thesecond view component 520.

After the property of the view component is set to the value of thesolved model variable, the view composition including the view componentis then rendered (act 1404).

If desired, a variety of environments may be used in connection with themethod 1400. For example, FIG. 15 illustrates an environment 1500 thatmay be used in connection with the method 1400 and may be implemented insoftware, hardware, or a combination. In particular, the environment1500 may include one or more solvers 1501 a, 1501 b, 1501 c, which mayform part of and/or be invoked by a property-setter 1502 a, 1502 b, 1502c. The solvers 1501 may be configured to solve for an unknown modelvariable (act 1401 of FIG. 14), for instance, by using a known modelvariable to solve for the unknown model variable. In addition, theproperty-setters 1502 may set a property of a view component to thevalue of the solved model variable (act 1403 of FIG. 14). Theproperty-setters 1502, however, need not include a solver 1501 and maysimply receive a known model variable and then set the property of theview component to the value of the received model variable.

FIG. 16 illustrates a flowchart of another method 1600 that may beperformed by the environment 1500 shown in FIG. 15. In further detail, afirst property-setter 1502 a of the environment 1500 is invoked (act1601). The first property-setter 1502 a and the other property-setters1502 may be invoked by and/or form part of the model-view bindingcomponent 510 shown in FIG. 5, if desired.

After being invoked, the first property-setter 1502 a sets a firstproperty of a view component of a view composition (act 1602). Forexample, the first property-setter 1502 a may set the first property tothe value of the model variable 1503 a. In some instances, the firstproperty-setter 1502 a may simply receive a known model variable 1503 aand then set the first property to the value of the received modelvariable. In other instances, the first property-setter 1502 a mayinvoke a solver 1501 a, which may solve for the model variable 1503 a,and then may set the first property to the value of the solved modelvariable.

In addition to setting the first property, the first property-setter1502 a also invokes a second property-setter 1502 b (act 1603). Thesecond property-setter 1502 b then invokes a solver, such as the solver1501 b, that may be configured to solve for a model variable (act 1604).In particular, as shown in FIG. 15, the solver 1501 b may be invoked byand/or form part of the second property-setter 1502 b, and when invoked,the solver 1501 b may solve for an unknown model variable 1503 b. Insome instances, the solver 1501 b may solve for an unknown modelvariable 1503 b by using a known model variable, for instance, the modelvariable 1503 a. For example, when the first property-setter 1502 ainvokes the second property-setter 1502 b (act 1603), the firstproperty-setter 1502 a could pass the model variable 1503 a to thesecond property-setter 1502 b. The solver 1501 b could then solve for anunknown model variable 1503 b by using the model variable 1503 a. Ofcourse, the first property-setter 1502 a need not pass the modelvariable 1503 a to the second property-setter 1502 b, which could accessthe model variable 1503 a in any other suitable fashion.

The second property-setter 1502 b then sets a second property of a viewcomponent of a view composition (act 1605), for instance, to the valueof the solved model variable 1503 b. Of course, in some instances, thesecond property-setter 1502 b may simply receive a known model variable1503 b, and the second property-setter may then set the second propertyto the value of the received model variable. Accordingly, the secondproperty-setter 1502 b need not invoke a solver (act 1604). After thesecond property-setter 1502 b sets the second property, the viewcomposition including the view component is then rendered (act 1606).

In some instances, the first property and the second property may beproperties of a single view component of the view composition.Accordingly, the property-setters 1502 a, 1502 b of the single viewcomponent may set the first and second properties (acts 1602, 1605) tothe values of the model variables 1503 a, 1503 b, thus allowing themodel variables 1503 a, 1503 b to define the first and secondproperties.

In other instances, the first property may be a property of a first viewcomponent, and the second property may be a property of a second viewcomponent. Accordingly, the property-setter 1502 a of the first viewcomponent may set the first property (act 1602) to the value of themodel variable 1503 a, thus allowing the model variable 1503 a to definethe first property. In addition, the property-setter 1502 b of thesecond view component may set the second property (act 1605) to thevalue of the model variable 1503 b, thus allowing the model variable1503 b to define the second property. If desired, the first viewcomponent may be a child or a parent of the second view component, andthe second view component may be a child or a parent of the first viewcomponent.

Thus, as shown above with respect to FIG. 11, a plurality of solvers maybe composed an explicit order (act 1107) and then solved according tothe order (act 1108). For example, the solvers may be explicitlycomposed using a relational structure, such as a dependency tree. Inaddition, as shown with respect to FIGS. 14 through 16, a plurality ofsolvers may be implicitly composed based on the ability ofproperty-setters 1502 having solvers 1501 to invoke otherproperty-setters 1502 having solvers 1501.

Composite View Composition

Referring to FIG. 2, the pipeline environment 200 also includes a modelimportation mechanism 241 that is perhaps included as part of theauthoring mechanism 240. The model importation mechanism 241 provides auser interface or other assistance to the author to allow the author toimport at least a portion of a pre-existing analytics-driven model intothe current analytics-driven model that the user is constructing.Accordingly, the author need not always begin from scratch whenauthoring a new analytics model. The importation may be of an entireanalytics-driven model, or perhaps a portion of the model. For instance,the importation may cause one or more or all of the following sixpotential effects.

As a first potential effect of the importation, additional model inputdata may be added to the pipeline. For instance, referring to FIG. 2,additional data might be added to the input data 211, the analytics data221 and/or the view data 231. The additional model input data might alsoinclude additional connectors being added to the data access component310 of FIG. 3, or perhaps different canonicalization components 330.

As a second potential effect of the importation, there may be additionalor modified bindings between the model input data and the modelparameters. For instance, referring to FIG. 4, the data-model binder 410may cause additional bindings to occur between the canonicalized data401 and the model parameters 411. This may cause an increase in thenumber of known model parameters.

As a third potential effect of the importation, there may be additionalmodel parameters to generate a supplemental set of model parameters. Forinstance, referring to FIG. 4, the model parameters 411 may be augmenteddue to the importation of the analytical behaviors of the importedmodel.

As a fourth potential effect of the importation, there may be additionalanalytical relationships (such as equations, rules and constraints)added to the model. The additional input data resulting from the firstpotential effect, the additional bindings resulting for the secondpotential effect, the additional model parameters resulting from thethird potential effect, and the additional analytical relationshipsresulting from the fourth effect. Any one of more of these additionalitems may be viewed as additional data that affects the viewcomposition. Furthermore, any one or more of these effects could changethe behavior of the solvers 440 of FIG. 4.

As a fifth potential effect of the importation, there may be additionalor different bindings between the model parameters and the inputparameters of the view. For instance, referring to FIG. 5, themodel-view binding component 510 binds a potentially augmented set ofmodel parameters 411 to a potentially augmented set of view componentsin the view component repository 520.

As a sixth potential effect of the importation, there may be additionalparameterized view components added to the view component repository 520of FIG. 5, resulting in perhaps new view items being added to the viewcomposition.

Accordingly, by importing all or a portion of another model, the dataassociated with that model is imported. Since the view composition isdata-driven, this means that the imported portions of the model areincorporated immediately into the current view composition.

When the portion of the pre-existing analytics-driven analytics model isimported, a change in data supplied to the pipeline 201 occurs, therebycausing the pipeline 201 to immediately, or in response to some otherevent, cause a regeneration of the view composition. Thus, upon what isessentially a copy and paste operation from an existing model, thatresulting composite model might be immediately viewable on the displaydue to a resolve operation.

As an example of how useful this feature might be, consider the FengShui room view composition of FIG. 7. The author of this application maybe a Feng Shui expert, and might want to just start from a standard roomlayout view composition model. Accordingly, by importing a pre-existingroom layout model, the Feng Shui expert is now relatively quickly, ifnot instantly, able to see the room layout 701 show up on the displayshown in FIG. 7. Not only that, but now the furniture and room itemcatalog that normally might come with the standard room layout viewcomposition model, has now become available to the Feng Shui applicationof FIG. 7.

Now, the Feng Shui expert might want to import a basic pie chart elementas a foundation for building the Feng Shui chart element 702. Now,however, the Feng Shui expert might specify specific fixed inputparameters for the chart element including perhaps that there are 8wedges total, and perhaps a background image and a title for each wedge.Now the Feng Shui expert need only specify the analytical relationshipsspecifying how the model parameters are interrelated. Specifically, thecolor, position, and type of furniture or other room item might have aneffect on a particular Feng Shui score. The expert can simply write downthose relationships, to thereby analytically interconnect the roomlayout 601 and the Feng Shui score. This type of collaborative abilityto build on the work of others may generate a tremendous wave ofcreativity in creating applications that solve problems and permitvisual analysis. This especially contrasts with systems that might allowa user to visually program a one-way data flow using a fixed dependencygraph. Those systems can do one-way solves, the way originallyprogrammed from input to output. The principles described herein allowsolves in multiple ways, depending on what is known and what is unknownat any time given the interactive session with the user.

Visual Interaction

The view composition process has been described until this point asbeing a single view composition being rendered at a time. For instance,FIG. 7 illustrates a single view composition generated from a set ofinput data. However, the principles described herein can be extended toan example in which there is an integrated view composition thatincludes multiple constituent view compositions. This might be helpfulin a number of different circumstances.

For example, given a single set of input data, when the solver mechanismis solving for output model variables, there might be multiple possiblesolutions. The constituent view compositions might each represent one ofmultiple possible solutions, where another constituent view compositionmight represent another possible solution.

In another example, a user simply might want to retain a previous viewcomposition that was generated using a particular set of input data, andthen modify the input data to try a new scenario to thereby generate anew view composition. The user might then want to retain also thatsecond view composition, and try a third possible scenario by alteringthe input data once again. The user could then view the three scenariosat the same time, perhaps through a side-by-side comparison, to obtaininformation that might otherwise be difficult to obtain by just lookingat one view composition at a time.

FIG. 17 illustrates an integrated view composition 1700 that extendsfrom the Feng Shui example of FIG. 7. In the integrated viewcomposition, the first view composition 700 of FIG. 7 is representedonce again using elements 701 and 702, exactly as shown in FIG. 7.However, here, there is a second view composition that is emphasizedrepresented. The second view composition is similar to the first viewcomposition in the there are two elements, a room display and a FengShui score meter. However, the input data for the second viewcomposition was different than the input data for the first viewcomposition. For instance, in this case, the position data for severalof the items of furniture would be different thereby causing theirposition in the room layout 1701 of the second view composition to bedifferent than that of the room layout 701 of the first viewcomposition. However, the different position of the various furnitureitems correlates to different Feng Shui scores in the Feng Shui meter1702 of the second view composition as compared to the Feng Shui meter702 of the first view composition.

The integrated view composition may also include a comparison elementthat visually represents a comparison of a value of at least oneparameter across some of all of the previously created and presentlydisplayed view compositions. For instance, in FIG. 13, there might be abar graph showing perhaps the cost and delivery time for each of thedisplayed view compositions. Such a comparison element might be anadditional view component in the view component repository 520. Perhapsthat comparison view element might only be rendered if there aremultiple view compositions being displayed. In that case, the comparisonview composition input parameters may be mapped to the model parametersfor different solving iterations of the model. For instance, thecomparison view composition input parameters might be mapped to the costparameter that was generated for both of the generations of the firstand second view compositions of FIG. 17, and mapped to the deliveryparameter that was generated for both of the generations of the firstand second view compositions.

Referring to FIG. 17, there is also a selection mechanism that allowsthe user to visually emphasize a selected subset of the total availablepreviously constructed view compositions. The selection mechanism 1710is illustrated as including three possible view constructions 1711, 1712and 1713, that are illustrated in thumbnail form, or are illustrated insome other deemphasized manner. Each thumbnail view composition 1711through 1713 includes a corresponding checkbox 1721 through 1723. Theuser might check the checkbox corresponding to any view composition thatis to be visually emphasized. In this case, the checkboxes 1721 and 1723are checked, thereby causing larger forms of the corresponding viewconstructions to be displayed.

The integrated view composition, or even any single view composition forthat matter, may have a mechanism for a user to interact with the viewcomposition to designate what model parameters should be treated as anunknown thereby triggering another solve by the analytical solvermechanism. For instance, in the room display 1701 of FIG. 17, one mightright click on a particular item of furniture, right click on aparticular parameter (e.g., position), and a drop down menu might appearallowing the user to designate that the parameter should be treated asunknown. The user might then right click on the harmony percentage(e.g., 95% in the Feng Shui score meter 1702), whereupon a slider mightappear (or a text box of other user input mechanism) that allows theuser to designate a different harmony percentage. Since this wouldresult in the identity of the known and unknown parameters beingchanged, a re-solve would result, and the item of furniture whoseposition was designated as an unknown might appear in a new location.

Interaction Visual Cues

In one embodiment, the integrated view composition might also include avisual prompt or cue associated with a visual item. The visual cue givessome visual indication to the user that 1) the associated visual itemmay be interacted with, 2) what type of interaction is possible withthat visual item, 3) what the result would be if a particularinteraction is made with the visual item, and/or 4) whether interactionwith one or more other visual items would be necessary in order toachieve the result.

As previously mentioned in reference to FIG. 5, the view portion 500includes a view repository 520 that includes multiple view components521, 522, 523, 524. Some of the view components 522, 523 and 524 aredriven by the values populated within corresponding input parameters(parameters 542A and 542B for view component 522, parameter 543 for viewcomponent 523, and parameter 544 for view component 524). The dataprovided to the input parameter(s) drive the execution logic of thecorresponding view component such that the data controls theconstruction of the visual item. For instance, the structure of thevisual item 552 may depend on the data provided to the input parameter542A and/or the input parameter 542B.

In one embodiment, one or more of the input parameters for a given viewcomponent may be an interactivity parameter. That interactivityparameter might define whether or not the visual item is to be renderedwith interactivity. Alternatively, or in addition, the interactivityparameter might cause a particular type of interactivity to be appliedto the corresponding visual item that is constructed as a result ofexecuting the execution logic of the corresponding view component. Aview component that contains at least one such interactivity parameterwill be referred to hereinafter as a “visually cued interactive” viewcomponent. In addition to enabling the interactivity, the data providedto the interactivity parameter might also define how the correspondingvisual item will be visually cued, when rendered, to visually inform theuser 1) that the visual item is interactive and potentially also thetype of interactivity. One, some or even all of the rendered visualitems may be interactive and visually cued in this manner.

There are a number of different types of interactivity that may beoffered by a visual item. One is referred to herein as navigationinteractivity. When a user navigationally interacts with a visual item,a subset of the view components that are used to construct visual itemsfor rendering changes.

For instance, one type of navigational interaction is referred to hereinas a scoping interactivity. The scoping interactivity changes the subsetof visual items that are rendered such that such that at least some ofthe visual items that were displayed just prior to the navigationinteractivity are also displayed just after the navigationinteractivity. For instance, referring to FIG. 5, the virtual space 500is illustrated as including four visual items 551, 552, 553 and 554. Ifthe visual item 552 has scoping interactivity, the user might interactwith the visual item 552 such that visual items 551 and 554 are nolonger in the virtual space 550 to be rendered. However, visual items552 and 553 might remain in the virtual space 550. Alternatively or inaddition, further visual items might be constructed into the visualspace.

One type of scoping interactivity is a scrolling interactivity thatchanges a range of the view components that are used to generate visualitems for rendering. FIGS. 18A and 18B represent an example of scrollinginteractivity. FIG. 18A represents virtual space 1800A before thescrolling interactivity. Here, the view composition component (see FIG.5) has constructed six visual items 1811, 1812, 1813, 1814, 1818 and1816. Visual item 1811 is adorned with a visual cue (represented by theasterisk 1801) that indicates that the visual item has scroll leftinteractivity enabled. Visual item 1816 is adorned with a visual cue(represented by the asterisk 1899) that indicates that the visual itemhas scroll right interactivity enabled. FIG. 18B represents the virtualspace 1800B after the user interacts with the visual item 1816 to scrollright. Here, the visual item 1811 is removed from the virtual space. Inaddition, a new visual item 1817 is added to the virtual space. In anexample of edit interactivity, the scrolling interactivity in this casealso caused the visual item 1812 to be adorned with a visual cuerepresented leftward scrolling interactivity is enabled. Furthermore,the visual item 1816 loses its visual cue and interactivity, which isprovided instead to the visual item 1817. The visual cue andinteractivity functionality may be enabled and disabled for a givenvisual item by simply changing data that is provided to populate inputparameter(s) of the corresponding view component.

Another type of scoping interactivity is a “detailing interactivity”that changes a level of detail of the view components that are used togenerate visual items for rendering. A “zoom-in” interactivity mightcause more specific granularities of visual items to appear, withperhaps some of the previous visual items disappearing from view. A“zoom-out” interactivity might cause courser granularities of visualitems to appear, with perhaps some of the prior more specificgranularities of items to disappear. For instance, if zooming in on amap of the visible universe, clusters of galaxies may begin to appear.If one is to zoom in on that cluster of galaxies, individual galaxiesmight begin to take form. If zooming in on one of those individualgalaxies, such as the Milky Way galaxy, individual stars may appear. Ifzooming in one of those stars, such as our sun, the detail of the sunmay become more and more apparent, with perhaps planets beginning toappear. If one is to zoom in on one of those planets, such as the Earth,large-scale topographical features, such as the continents, may appear.If one is to zoom in further, country boundaries may appear, later townsmay appears, then streets may take form. This may continue untilsub-atomic particles take form. The coarseness of granularities in suchzooming topographies need not be physically related, as in the model ofthe universe. One may navigate through other topographies as well witheach scoping interactivity causing previous visual items to disappearand causing new visual items to appear. Once again, the use of apseudo-infinite data series may facilitate this operation.

FIG. 19A illustrates an example virtual space 1900A before aparticularly detailing interactivity operation. Here, there are only twovisual items 1911 and 1912 begin displayed. As seen in the virtual space1900B of FIG. 19B, the visual item 1911 is enlarged, and now some visualitems 1921 and 1922 are rendered inside of the visual item 1911, and thevisual item 1912 now falls outside of view. This is an example of azoom-in interactivity. For an example of a zoom-out interactivity, onemight begin with the virtual space 1900B of FIG. 19B, with the virtualspace 1900A representing the state after the zoom-out interactivity.

The type of interactivity might also be a linking interactivity thatcauses at least one rendered frame (and perhaps the entire display) todisplay completely different visual items that were displayed before.For instance, in the Feng Shui room example above in FIG. 7, clicking ona particular visual item (such as the Feng Shui meter 702) might cause aweb page about Feng Shui to be displayed in place of the Feng Shui roomview. Alternatively, perhaps there is a visual item that if interactedwith might cause a different room entirely to appear.

Yet another type of interactivity might be an external actioninteractivity that causes some action to be taken that is independent ofthe visual scene that is rendered. For instance, a visual item might beinteracted with that might cause an e-mail to be sent, or set an alert,schedule a data backup, perform a performance check, and so forth.

The type of interactivity might also be an edit interactivity. An editinteractivity changes data in a manner that one or more input parametervalues of one or more view components changes. If that input parameteraffects how the a visual item is constructed, then the visual itemchanges as a results. A change in data may also cause a value of aninput model parameter to change, or cause the identity of the inputmodel parameters and/or output model parameters to change. Thus, an editinteractivity imposed on a visual item may cause an entirere-computation of the analytics portion 400. Several examples of editinteractivity will now be provided.

FIG. 20 illustrates a rendering of the contiguous United States 2000.The United States visual item may be constructed from one viewcomponent. However, the constituent states may each be constructed froma corresponding child view component. The elevation of the visual itemcorresponding to that state represents some parameter of the state(e.g., consumption per capita of a particular product under evaluation).Here, the New Mexico visual item 2001 has a visual cue in the form of anupward facing arrow 2011. This prompts the user that the New Mexicovisual item 2001 may be interacted with such that the height of thevisual item may be altered. Also, the Nevada visual item 2002 and theFlorida visual item 2003 having corresponding downward facing arrows2012 and 2013 respectively. These arrows might visually cue the userthat an upward adjustment in the height of the New Mexico arrow 2011would, after reanalysis of the model through the analytics portion 400,cause a downward adjustment in the heights of the Nevada visual item2002 and the Florida visual item 2003. Alternatively or in addition,arrows 2012 and 2013 could indicate that if both of the heights of theNevada visual item 2002 and the Florida visual item 2003 are adjusteddownwards by the user, then the height of the New Mexico visual item2001 will be adjusted upwards. In order to determine the consequence ofa particular user interaction, the pipeline 201 might consider how theuser might interact with the rendered visual items, and perform acontingency solve of the analytical model to determine what theconsequences would be.

FIG. 21 illustrates a chart 2100 that includes a related bar chart 2110and pie chart 2120. The bar chart 2110 includes multiple bars. One ofthe bars 2111A is illustrated with an arrow 2111B to represent that thisheight may be adjusted vertically. This might cause some adjustment inallocations of the various wedges in the pie chart 2120. The bar chart2110 and the pie chart 2120 may be visually merged. For instance, theresulting pie chart might include wedges whose thickness depend on theheight of the associated bar of the bar chart.

FIG. 22 illustrates a hurricane mapping chart 2200 in which several thepath 2201 of a hurricane 2211 is being charted. A visual cue 2220indicates that the user may interactive with the path 2201 so as tochange the path 2101 to, for example, path 2202. This allows the user toevaluate possible alternative realities for the path of the hurricane.Controls 2221, 2222, 2223 and 2224 also allow various parameters of thehurricane to be altered such as, for example, wind speed, temperature,spin, and hurricane migration speed.

In the Feng Shui example of FIG. 7, if a particular harmony score isdesignated as a known input parameter, various positions of thefurniture might be suggested for that item of furniture whose positionwas designated as an unknown. For instance, perhaps several arrows mightemanate from the furniture suggesting a direction to move the furniturein order to obtain a higher harmony percentage, a different direction tomove to maximize the water score, a different direction to move tomaximum the water score, and so forth. The view component might alsoshow shadows where the chair could be moved to increase a particularscore. Thus, a user might use those visual prompts in order to improvethe design around a particular parameter desired to be optimized. Inanother example, perhaps the user wants to reduce costs. The user mightthen designate the cost as an unknown to be minimized resulting in adifferent set of suggested furniture selections.

FIG. 23 illustrates a flowchart of a method for interacting with a userinterface that displays a plurality of visual items. The computerrenders data-driven visual items on a display (act 2301) as previouslydescribed. Recall that each of the data-driven visual items areformulated by providing data to parameterized view components. Thatdata, in turn, may have been obtained by the analytical model inresponse to data being provided to the analytics portion 400 of thepipeline 201, or in response to data being provided to the data portion300 of the pipeline 201. In addition, one, some, or all of the visualitems have a visual cue or other visual emphasis (act 2302) conveying tothe user that there the visual item may be interacted with and/or thetype of interactivity. In one embodiment, the visual cue initially onlyrepresents that the corresponding visual item is interactive, and it isnot until the user selects the visual item (by hovering over the visualitem with the pointer) that the visual cue is modified or supplementedto convey the type of interactivity.

The computing system then detects that a predetermined physicalinteraction between a user and the visual item has occurred (act 2303).In response, the interactivity is enabled or activated (act 2304) forthat particular visual item. The appropriate response will depend onwhether the interactivity is a navigation interactivity, a linkinginteractivity, or an edit interactivity. In the case of an editinteractivity, the result will also depend on the analyticalrelationship between the various visual items as defined by theanalytics portion 400 of the pipeline.

The interactivity might be a combination of two or more of navigation,linking, and editing interactivities. For instance, in the United Statesexample of FIG. 20, an upward adjustment of the height of the New Mexicovisual item 2001 might result in a downward adjustment of the heights ofthe Nevada visual item 2002 and the Florida visual item 2003(representing an example of edit interactivity). However, this mightalso result in the display splitting into four frames, with three frameseach zooming in one the three states Nevada, New Mexico and Florida (anexample of scoping interactivity). In addition, the fourth frame mightcontain survey information regarding consumption preferences of theresidence of New Mexico (an example of linking interactivity).

FIG. 24 abstractly illustrates a user interface 2400 that representsanother application example. In this application example, a convenientuser interface is described that allows a user to easily constructdata-driven visual scenes using the principles described herein.

The user interface 2400 includes a first set 2411 of visual item(s). Inthis illustrated case the set 2411 includes but a single visual item2411. However, the principles described herein are not limited to justone visual item within the visual item set 2411. The visual item(s) 2411have associated data 2412, and thus will sometimes be referred to hereinas “data visual item(s)” 2411.

Although not required, in this example, the associated data issubdivided into multiple data groups. Any number of groups will suffice.However, four data groups 2413A, 2413B, 2413C and 2413D are illustratedas being included within the associated data 2412 of FIG. 24. In theillustrated associated data 2412, each of the data groups is organizedin parallel with each having associated multiple data fields. In theillustrated case, each of the data groups in the illustrated associateddata 2412 includes corresponding fields a, b and c. Fields a, b, and cof data group 2413A will hereinafter be referred to as data fields2413Aa, 2413Ab and 2413Ac, respectively. Fields a, b, and c of datagroup 2413B will hereinafter be referred to as data fields 2413Ba,2413Bb and 2413Bc, respectively. Fields a, b, and c of data group 2413Cwill hereinafter be referred to as data fields 2413Ca, 2413Cb and2413Cc, respectively. Finally, fields a, b, and c of data group 2413Dwill hereinafter be referred to as data fields 2413Da, 2413Db and2413Dc, respectively.

The user interface 2400 also includes a second set 2420 of visual items.In the illustrated case, the set 2420 includes three correspondingvisual items 2421, 2422 and 2423. However, there is not a limit to thenumber of visual items in the second set 2420, nor is there anyrequirement that the visual items in the second set be of the same type.Hereinafter, the visual items 2421, 2422, and 2423 may also be referredto as “element visual items”. Each visual item 2421, 2422 and 2423 maybe, for example, constructed by executing corresponding logic of arespective view component using input parameters. Such view componentsmay be similar to those described with respect to FIG. 5 for the viewcomponents 521 through 524. Thus, each of the visual items 2421, 2422and 2423 is illustrated as having input parameters. Such inputparameters may be the input parameters provided to the view component,or might be some other input parameter that drives the rendering of thevisual item. As an example only, each of the visual items 2421, 2422 and2423 are illustrated as each having three input parameters.Specifically, visual item 2421 is illustrated as having input parameters2421 a, 2421 b and 2421 c. Visual item 2422 is illustrated as havinginput parameters 2422 a, 2422 b and 2422 c. Visual item 2423 isillustrated as having input parameters 2423 a, 2423 b and 2423 c.

The user interface 2400 also includes a user interaction mechanism 2440.The user interaction mechanism 2440 permits the user (through one ormore user gestures) to cause the data 2412 of the data visual item(s)2411 to be applied to the input parameters of the element visual items2420. In one embodiment, the user gesture(s) may actually cause theassociated data to be bound to the input parameters of the elementsvisual items 2420. Such user gestures might be a drag or drop operation,a hover operation, a drag and click, or any other user gesture orcombination of gestures. Thus, a user may apply or bind data from thedata visual item(s) 2410 to the input parameters of the element visualitems, thereby changing the appearance of the element visual items,using simple gestures. In one embodiment, this does not even involve auser typing in any of the associated data, and allows a single set ofgestures to apply and/or bind data to multiple element visual items.Examples of the user of user gestures to apply or bind data from onedata visual item to multiple element visual items will be describedfurther below.

In one embodiment, the user interaction mechanism 2440 permits the userto apply the data 2412 from the data visual item 2411 to the inputparameters of the element visual items 2420 on a per data group basessuch that 1) one data group of each of the data groups is applied to theset of one or more input parameters for a distinct visual item of thesecond plurality of visual items, and 2) a single field of the same typefrom each data group of the plurality of data groups is applied to aninput parameter of the set of one or more input parameters for adistinct visual item of the element visual items.

As an example, referring to FIG. 24, the user might use a single set ofgestures to simultaneously apply or bind the data field 2413Aa of thedata visual item(s) 2411 to input parameter 2421 b of the element visualitem 2421, the data field 2413Ba of the data visual item(s) 2411 toinput parameter 2422 b of the element visual item 2422, and data field2413Ca of the data visual item(s) 2411 to input parameter 2423 b of theelement visual item 2423. If there were a fourth element visual item,the data field 2413Da could have been applied to the corresponding inputparameter for the fourth element visual item.

As a result of this same set of gestures, or perhaps in response toadditional sets of gestures, further applications or binding may bemade. For instance, in response to user gestures, the data field 2413Abof the data visual item(s) 2411 could be applied or bound to inputparameter 2421 a of the element visual item 2421, the data field 2413Bbof the data visual item(s) 2411 could be applied or bound to inputparameter 2422 a of the element visual item 2422, and data field 2413Cbof the data visual item(s) 2411 could be applied or bound to inputparameter 2423 a of the element visual item 2423. Additionally, the datafield 2413Ac of the data visual item(s) 2411 could be applied or boundto input parameter 2421 c of the element visual item 2421, the datafield 2413Bc of the data visual item(s) 2411 could be applied or boundto input parameter 2422 c of the element visual item 2422, and datafield 2413Cc of the data visual item(s) 2411 could be applied or boundto input parameter 2423 c of the element visual item 2423.

The user interface 2400 potentially also includes a third visual item2430 having associated properties 2431. A second user interactionmechanism 2450 permits the user to use a set of gesture(s) to merge thesecond set of visual items 2420 into the third visual item 2430 suchthat 1) one or more input parameters of each the second offset 2420visual items are set using the associated properties 2431 of the thirdvisual item 2430 and/or 2) the properties 2431 of the third visual item2430 are used to alter the one or more input parameters of each of thesecond set 2420 of visual items. The gestures used to accomplished thismay be a drag and drop operation, a hover operation, or any other usergesture, and may have even been accomplished using, in whole or in part,the same user gestures that were used to apply the data from data visualitem(s) 2410 to the element visual items 2420.

For example, if the user gesture(s) (i.e., the second set of gesture(s))that are used to merge the second set 2420 of visual items and the thirdvisual item 2430 at least partially overlap with the user gesture(s)(i.e., the first set of gesture(s)) that are used to apply or bind datafrom the data visual item(s) 2411 to the input parameters of the elementvisual items 2420, then there is at least one common gesture within boththe first set of gesture(s) and the second set of gesture(s). However,this is not required at all. There may be, in fact, no common gesture(s)between the first set of gestures and the second set of gesture(s).Thus, distinct user action might be employed to apply data from the datavisual item 2411 to the element visual items 2420 as compared to mergingthe element visual items 2420 with the visual item 2430.

In one embodiment, the data visual item(s) 2411 may each be similar tothe visual items constructed in FIG. 5 using view construction modules521 through 524 of FIG. 5. In that case, the associated data 2412 may bedata provided to input parameter(s) of the corresponding view component.This associated data 2412 may even be surfaced as visual in the visualitem itself allowing the user to have a view on the associated data. Thevisual item 2430 may also be similar to the visual item constructedusing a view construction module 521 through 524. In that case, perhapsone or more of the properties 2431 of the visual item 2430 may be setusing input parameters of the corresponding view component.

The application of the associated data 2412 of the data visual item(s)2411 to the input parameters of the element visual items 2420 may causean analytical model to resolve. For instance, FIG. 4 describes ananalytics portion 400 of the pipeline 200. By applying the applicationof the associated data 2412, there might be data that needs to berecalculated in order to further repopulate the input parameters of theelement visual items 2420. In addition, the merging of the elementvisual items 2420 with the third visual item 2430 might also cause are-solve of the analytics portion 400 to thereby alter input parametersof the visual items 2420 or 2430.

Of course, FIG. 24 is an abstract representation of a user interface. Amore concrete example of a user interface that permits a user todesignate associated data of a data visual item(s) to be applied toelement visual items, and that allows element visual items to be mergedwith another higher-level visual item will now be presented with respectto FIG. 25 through 29.

FIG. 25 illustrates a user interface 2500 in which a helix 2530 ispresented. The helix 2530 is a concrete example of the third visual item2430. In this case, the properties of the helix 2530 (as an example ofthe properties 2431) might be the radius of curvature, the pitch (orangle of ascension), the color, the line thickness, the linecross-sectional profile, the winding length, the beginning angle, and soforth. Each of the properties might be, for example, input propertiesprovided to a helix view composition element that has construction logicthat, when executed, renders the helix. The helix shape type might beone of several shapes that may be dragged and dropped into the worksurface 2501 of the user interface. The helix may have its inputparameters populated with default values upon being dragged into thework surface, but may have those default values changed.

The user may have also dragged a separate cuboid object 2521 onto thework surface. The cuboid object 2521 is an example of an element visualobject 2421 of FIG. 24. A cuboid object 2521 typically has sixrectangular-shaped sides that are parallel or perpendicular to eachother. Had the cuboid object 2521 been dragged into another part of thework surface other than on the helix 2530, then the cuboid object 2521might have retained those fundamental cuboid characteristics. However,in this case, the user has gestured (perhaps through dragging the cuboid2521 onto a portion of the helix 2530 using the pointer 2550) that thecuboid object 2521 and the helix 2530 are to be merged. In this case,the cubiod 2521 has its input parameters adjusted such that its centralline follows the helix. This might also be a mechanism for defining atype of the element visual item (e.g., a cuboid in this case) that is tobe merged with the helix visual item.

Given this merging operation, FIG. 26 illustrates another stage 2600 ofthis particular example of the user interface. Here, the user hasacquired a data visual item 2610, in this case, perhaps a spreadsheetdocument. The spreadsheet document 2610 is an example of the data visualitem 2411 of FIG. 24. This spreadsheet document contains a table listingvarious psychiatric drugs. The data is entirely fictional, but isprovided to illustrate this example. The fourth column 2614 lists thename of the drug, one for each row, although in this case, the fieldsare blank simply because the data is fictional. The third column 2613lists the category of the drug corresponding to each row. The firstcolumn 2611 lists the start date (in years) that the drug correspondingto each row was approved for prescription use. The second column 2612lists the duration (in years) that the drug corresponding to each rowwas approved for use. The start date, the duration, the category and thename are examples of the fields of the data groups of the associateddata 2412 of FIG. 24. The rows in the spreadsheet 2610 are examples ofthe data groups of the associated data 2412 of FIG. 24.

Here, an input parameters table 2620 appears to show the user what inputparameters have been selected as being a target for population. The userhas selected the first column 2611 (i.e., the Start Data field) topopulate the “Position on Helix” input parameter. A cuboid visual itemis generated for each data group (each row in the chart 2610). Thepreview 2630 shows the helix 2530, but with a preview of what the mergedversion of the multiple cuboid elements and the helix would look like.

FIG. 27 shows the user interface 2700. Here, the user has selected thatthe various cuboids are not to be cropped, but are to be stacked one ontop of the other with the helix (now invisible) serving as a base forthe stack. The stacked cuboids 2710 represents an example of theelements visual items. Note that properties of the helix visual itemhave been inherited by the input parameters of each cuboid. In addition,the data from the data visual item 2610 have been inherited by eachcuboid. For instance, each cuboid in the stack of cuboids wasconstructed using a position on the helix corresponding to the startdate of the corresponding drug. The length of each cuboid is inheritedby the duration of the corresponding drug. In addition, the cuboidinherits position properties and curvature properties from the helix.

FIG. 28 shows the complete visual scene. In this user interface 2800,color may be assigned to each curved cuboid according to the category ofthe corresponding drug. Thus, the start date of the corresponding drugdefines the beginning position of the curved cuboid in the helix. Forinstance, the user might have used the following gestures to apply thestart date data from the spreadsheet visual item 2411 to the cuboidvisual items: 1) select the first column of the spreadsheet, and 2) dragthe column into the “Position on Helix” section of the input parametertable 2620. Furthermore, the duration of the corresponding drug definesthe length of the curved cuboid in the helix. For instance, the usermight have used the following gestures to apply the duration data fromthe spreadsheet visual item 2411 to the cuboid visual items: 1) selectthe first column of the spreadsheet, and 2) drag the column into the“Length” section of the input parameter table 2320. Finally, thecategory of the corresponding drug defines the color of the curvedcuboid in the helix. The user might have used the following gestures toapply the category data from the spreadsheet visual item 2411 to thecuboid visual items: 1) select the first column of the spreadsheet, and2) drag the column into the “Color” section of the input parameter table2620. The user could drag and drop different fields of the spreadsheetinto difference sections of the input parameter field to see what visualrepresentation of the data is best given the circumstances.

Using these principles described herein, complex geometries may beconstructed and composed with other visual elements and geometries. Inorder to understand the composition of geometries, four key conceptswill first be described. The key concepts include 1) data series, 2)shapes, 3) dimension sets, and 4) geometries.

First, a data series will be described. A data series is a wrapper ondata. An example of a data series object was illustrated and describedwith respect to FIG. 6. A data series object is not the data itself.However, the data series knows how to enumerate the data. For instance,referring to FIG. 6, the enumeration module 601 enumerates the dataseries corresponding to the data stream object. In addition, the dataseries has attributes that declare: the range, quantization, andresolution of the plot. Examples of data types that may be wrapped bythe data series include a table column, repeating rows in a table, ahierarchy, a dimensional hierarchy, and so forth.

A shape can be a canonical visual item constructed from a canonical viewcomposition. Examples of such canonical visual items include, forexample, a point, bar, prism, bubble, a surface patch, am image file, a2 dimensional, or 3 dimensional shape, and so forth. However, the shapemay be a canonical construction from data. Examples, of such a canonicalconstruction from data include a label. Alternatively, a shape might bethe result of populating a geometry (the term geometry is to bedescribed further hereinbelow) with a data series and shapes.

Canonical shapes carry metadata that potentially allows a geometry's“binder-arranger” (described below) to be parameterized to handlemultiple shapes. The metadata also provides hints for “layout helpers”also described below. The metadata denotes aspects of the shape such aswhether the shape is rectilinear or curvilinear, whether the shape isplanar or volume occupying, whether the shape is symmetrical about somedimension, whether the shape needs to be read in the textual sense (e.g.a label) shapes, whether the shape can be colored or superimposed withtexture, and so forth.

Note that a Shape has no absolute dimensions, but may optionally specifyproportions in its dimensions. For instance, a prism may be specified tohave a base L and W that is some minimum percentage of its height H. Ashape may optionally specify constraints in how multiple instances ofthe shape may be laid out such as, for example, the minimum distancebetween two bars.

Regarding the dimension set, this is distinguished from a coordinatesystem. The dimension set contains as many dimensions as there aredimensions of data to be visualized. The dimensions may be more than theusual Euclidean axis x, y and z, and time t. In a dimension set, somemay be Euclidean (e.g. Cartesian or Polar x,y,z, or map coordinates witha z for height). Later, when used by a geometry, the effect of theseEuclidean axes is to delineate and control how shapes within a ShapeInstance Series are inserted or transformed in at some coordinates, withappropriate scaling, transformation, and distribution. Other dimensionsare manifested via layout effects like clustering and stacking. Yetother dimensions may involve higher visual dimensions like animation, ormotion speed.

As an example, consider visual items that include existing countries.The visual items might each correspond to a shape (describing the shapeof the country), and the polar coordinates of the center of the country.There might also be a data series that is to be rendered according tocolor of the country. For instance, blue might represent a small amountof resources expended in foreign aid on that country. Red mightrepresent a larger amount of resources expended in foreign aid on thatcountry.

Yet another dimension may be animated. For instance, there might be aspinning top associated with each country. That spinning top dimensionfor each country may be populated with data regarding the country'spolitical stability, regardless of how that political stability score isderived. Countries with less political stability may have the topanimated with a slower motion and greater wobbliness. Countries withgreater political stability may have the top animated with faster motionand less wobbliness. Other non-Euclidean dimensions might includetexture.

The dimension set may include a declaration of visibility/conditionalhiding, scrollability, titling, labeling, subdivision, plottablegranularity, permitted range (e.g. whether negatives are allowed), andso forth.

A “Geometry” consists of a container and one or more Binder-Arrangers.

As for containers, a geometry contains the description the visualelements/layout of the container in which the data will be visualized bymeans of shapes. For example, the simplest possible bar chart specifiesthe shape of the bounding rectangle, proportionality in the 2/3Ddimensions of the container (alternatively, absolute dimensions), thecoordinate system (e.g., Cartesian). A more sophisticated visualizationmay specify additional concepts. For instance, a map may specifysubdivisions within the geometry (like zones, streets) that restrictwhere within the coordinate system a shape may be placed. A quadrantdiagram may specify that its Cartesian axes accommodate negative values,color, texture, transparency, and other controllable visual parameters.

A Container may optionally specify metadata that potentially allows asignificant part of the intelligence needed in the correspondingbinder-arranger to be factored out to a small set of (oftensolver-based) Layout Helpers. The metadata denotes aspects of thecontainer such as, for example: whether the Euclidean axes (there aremore axis types) are rectilinear or curvilinear, whether the containeris planar or volume occupying, whether the container is symmetricalabout some dimension, and so forth.

Secondly, a Geometry carries with it a declaration of a“binder-arranger” that knows how to 1) Generate a shape instance seriesby applying the passed in Data Series and original Shape(s) to theDataShapeBindingParams that describe how data values map to dimensional(e.g. height) or visual attributes (e.g. color) of the Shape, and/or howto select from a set of shapes based on data value, and 2) Map thepassed in Axis Set to the coordinate system of the Container, and toother visual elements (stacking, clustering, coloring, motion, etc.) ofthe Container, 3) layout the shape instance series onto one or moredeimensions as mapped into the container, per theShapeAxisBindingParams, and 4) interpolate between laid out shapes wherenecessary, e.g. to connect lines, or create a continuous surface fromlittle surface-lets or patches.

A populated Geometry (i.e. a Geometry that is instantiated with one ormore data series and corresponding shapes) may itself be treated as ashape for the purpose of being passed into another Geometry. Forexample, in a stacked/clustered bar chart: the innermost geometry has asimple container, a bar in which other bars can be stacked. Populatingthe innermost geometry results in shapes that go into a second Geometrywhere the container is a cluster of bars. Finally, the Shape resultingfrom populating of the second Geometry is fed to the outermost geometry,that knows how to layout shapes along a horizontal axis, and also showtheir height on the y axis.

In the above stacked/clustered bar chart example, the innermost geometryjust had height, the second geometry had (sorted or unsorted)clustering, and the outermost geometry had a simple (i.e. unstacked)vertical dimension and a simple (i.e. unaware of clustering) horizontaldimension. However, the composite stacked/clustered bar chart can alsobe treated as a single geometry that can take in a dimension set withthree aspects: a stack-aware height dimension, a cluster dimension, anda horizontal X dimension.

Layout helpers rely on algorithms or solvers, and help determine the‘correct’ or ‘optimal’ positioning of shapes within a containinggeometry, as well as interpolation between shapes. As mentioned above,the idea of layout helpers is to reduce the specificity withinbinder-arrangers. There are two kinds of layout helpers, local layouthelpers that are invoked at the level of a particular binder-arranger,and global layout helpers that look over an entire containing geometry(including at the interim results of multiple B-A's putting shapeswithin the geometry).

Thus, the pipeline enables complex interactions across a wide variety ofdomains.

ADDITIONAL EXAMPLE APPLICATIONS

The architecture of FIGS. 1 and 2 may allow countless data-drivenanalytics model to be constructed, regardless of the domain. There isnothing at all that need be similar about these domains. Wherever thereis a problem to be solved where it might be helpful to apply analyticsto visuals, the principles described herein may be beneficial. Up untilnow, only a few example applications have been described including aFeng Shui room layout application. To demonstrate the wide-rangingapplicability of the principles described herein, several additionalwide-ranging and diverse example applications will now be described.

Additional Example #1 Retailer Shelf Arrangements

Product salespersons often use 3-D visualizations to sell retailers onshelf arrangements, end displays and new promotions. With the pipeline201, the salesperson will be able to do what-ifs on the spot. Given someproduct placements and given a minimum daily sales/linear footthreshold, the salesperson may calculate and visualize the minimumrequired stock at hand. Conversely, given some stock at hand and given abi-weekly replenishment cycle, the salesperson might calculate productplacements that will give the desired sales/linear foot. The retailerwill be able to visualize the impact, compare scenarios, and compareprofits. FIG. 29 illustrates an example retailer shelf arrangementvisualization. The input data might include visual images of theproduct, a number of the product, a linear square footage allocated foreach product, and shelf number for each product, and so forth. Theexample of FIG. 29 illustrates that application of charts within virtualworlds. Here, the plan sheet 2901 and the shelf layout 2902 may beanalytically related such that changes in the shelf layout 2902 affectthe plan sheet 2901, and vice versa.

Additional Example #2 Urban Planning

Urban planning mash ups are becoming prominent. Using the principlesdescribed herein, analytics can be integrated into such solutions. Acity planner will open a traffic model created by experts, and drag abridge in from a gallery of road improvements. The bridge will bringwith it analytical behavior like length constraints and high-windoperating limits. Via appropriate visualizations, the planner will seeand compare the effect on traffic of different bridge types andplacements. The principles described herein may be applied to any mapscenarios where the map might be for a wide variety of purposes. The mapmight be for understanding the features of a terrain and findingdirections to some location. The map might also be a visual backdrop forcomparing regionalized data. More recently, maps are being used tocreate virtual worlds in which buildings, interiors and arbitrary 2-D or3-D objects can be overlaid or positioned in the map. FIG. 30illustrates an example visualized urban plan. Note that once again,charts may be functionally and analytically integrated with a map of avirtual world (in this case a city). As the chart changes, so might thevirtual world, and vice versa.

Additional Example #3 Visual Education

In domains like science, medicine, and demographics where complex dataneeds to be understood not just by domain practitioners but also thepublic, authors can use the principles described herein to create datavisualizations that intrigue and engage the mass audience. They will usedomain-specific metaphors, and impart the authors' sense of style. FIG.31 is an illustration about children's education. FIG. 32 is aconventional illustration about population density. Conventionally, suchvisualizations are just static illustrations. With the principlesdescribed herein, these can become live, interactive experiences. Forinstance, by inputting a geographically distributed growth pattern asinput data, a user might see the population peaks change. Somevisualizations, where the authored model supports this, will let usersdo what-ifs. That is, the author may change some values and see theeffect on that change on other values.

Additional Example #4 Apply View Components to Parametric Targets

In some instances, it may be desirable to apply view components tovarious parametric targets (e.g., other view components), for example,as shown in FIGS. 33 and 34. In particular, the height of the panels inboth FIGS. 33 and 34 represent Napoleon's army size during his ill-fatedRussian campaign of 1812. In FIG. 33, the panels are applied to aparametric target that represents the actual path that Napoleon's armytook. In FIG. 34, the panels are applied to a different parametrictarget: a helix. Thus, the view components (such as, the panels) may beapplied to different parametric targets (such as, a view componentrepresenting the actual path that Napoleon's army took or a viewcomponent representing a helix).

The view components may be children of the parametric targets to whichthey are applied. For example, the panels in FIG. 33 may be children ofthe army path, while the panels in FIG. 34 may be children of the helixin FIG. 34. In addition, a label representing an army size may be achild of a panel whose height represents that army size.

Desirably, solvers tied to the properties of the child view componentsand solvers tied to the properties of the parent view components may beexplicitly composed through a dependency tree or implicitly composedthrough the interrelationship of property-setters that include suchsolvers.

Thus, a setting of a property in a child may trigger the re-solving of aproperty in a parent (sometimes referred to as “escalation”), and asetting of a property in a parent may trigger the re-solving of aproperty in child (sometimes referred to as “delegation”). For instance,with reference to FIG. 33, a user may attempt to alter the height of apanel, which may trigger a property-setter for the panel to increase thepanel's scale. In addition, the panel's scale property-setter may invokea property-setter for the scale for the set of panels (an escalation).The scale property-setter for the set of panels may then invokeindividual scale property-setters for each of the panels (a delegation).

Note in particular, FIG. 33 is an example of the application of a chartto virtual worlds. A “virtual world” is a computerized representation ofsome real of fictitious map. The map might be two-dimensional or may bethree-dimensional. For instance, a city map might include streets,buildings, rivers, and so forth all laid out in a geographicrelationship. “Charts” on the other hand represent data series appliedto variables. Charts may be applied to virtual worlds using theprinciples described herein by applying a data series represented by thechart to some aspect of a virtual world. That virtual world aspect mightbe horizontal or vertical surface, external services ofthree-dimensional objects, routes, and so forth. The virtual world mayrepresent a real physical space or might just represent a hypotheticalarea. The virtual world might represent all of a portion of a town,city, state, province, region, building, neighborhood, planet, or anyother physical area.

For instance, in the example of FIG. 33, the chart data includes foreach of a number of periods of time, the number of people in theNapoleon's army at that point in time. This information may be appliedto a route of Napoleon's army. The height of each bar represents thenumber of soldiers in Napoleon's army in a particular segment of thearmy's journey. If desired, other geographical features may be includedin the map such as rivers, cities, oceans and so forth. Charts may beapplied to virtual worlds by applying a chart feature, such as acoordinate system, axis or marker to a virtual world feature, such as asurface.

As other examples of the application of charts to virtual worlds,suppose a company has data representing employee moral. This chart datamay be applied to a virtual world such as a three-dimensional renderingof the company campus by rendering the color of the window of eachemployee a certain color according to moral. For instance, a blue windowmight represent a happy employee, whereas a red window might representan unhappy employee. Appropriate analysis may then be made. Forinstance, one might take a high-level view of the campus map, anddetermine what buildings are tending more red. It may be, perhaps, thatone of the buildings is isolated from the others, resulting in lessopportunity for interaction, and lower moral. The company might thenreconsider renting that building in the future, and try to procure abuilding for those employees that is closer to the whole.

Accordingly, the principles described herein provide a major paradigmshift in the world of visualized problem solving and analysis. Theparadigm shift applies across all domains as the principles describedherein may apply to any domain.

Domain-Specific Taxonomy of Data

Referring back to FIG. 2, the pipeline 201 is data-driven. For instance,input data 211 is provided to data portion 210, analytics data 221 isprovided to analytics portion 220, and view data 231 is provided to viewportion 230. Examples of each of these data have already been described.Suffice it to say that the volume of data that could be selected by theauthoring component 240 may be quite large, especially given the ease ofcomposition in which portions of models can be imported into a model tocompose more and more complex models. To assist in navigating throughthe data, so that the proper data 211, 221 and 231 may be selected, ataxonomy component 260 provides a number of domain-specific taxonomiesof the input data.

FIG. 35 illustrates a taxonomy environment 3500 in which the taxonomycomponent 260 may operate. Taxonomy involves the classification of itemsinto categories and the relating of those categories. The environment3500 thus includes a collection of items 3510 that are to be subjectedto taxonomization. In FIG. 35, the collection of items 3510 isillustrated as including only a few items altogether including items3511A through 3511P (referred to collectively as “member items 3511”).Although only a few member items 3511 are shown, there may be any numberof items, perhaps even hundreds, thousands or even millions of itemsthat should be categorized and taxonomized as represented by theellipsis 3511Q. The member items 3511 include the pool of member itemsfrom which the authoring component 240 may select in order to providethe data 211, 221 and 231 to the pipeline 201.

A domain-sensitive taxonomization component 3520 accesses all or aportion of the member items 3511, and also is capable of generating adistinct taxonomy of the member items 3511. For instance, thetaxonomization component 3520 generates domain specific taxonomies 3521.In this case, there are five domain-specific taxonomies 3521A through3521E, amongst potentially others as represented by the ellipsis 3521F.There may also be fewer than five domain-specific taxonomies created andmanaged by the taxonomization component 3520.

As an example, the taxonomy 3521A might taxonomize the member itemssuitable for a Feng Shui domain, taxonomy 3521B may taxonomize themember items suitable for a motorcycle design domain, taxonomy 3521C maylikewise be suitable for a city planning domain, taxonomy 3521D may besuitable for an inventory management domain, and taxonomy 3521E may besuitable for an abstract artwork domain. Of course, these are just fiveof the potentially countless number of domains that may be served by thepipeline 201. Each of the taxonomies may use all or a subset of theavailable member items to classify in the corresponding taxonomy.

FIG. 36 illustrates one specific and simple example 3600 of a taxonomyof the member items. For example, the taxonomy may be thedomain-specific taxonomy 3521A of FIG. 35. Subsequent figures will setforth more complicated examples. The taxonomy 3600 includes categorynode 3610 that includes all of the member items 3511 except member items3511A and 3511E. The category node 3610 may be an object that, forexample, includes pointers to the constituent member items and thus inthe logical sense, the member items may be considered “included within”the category node 3610. The category node 3610 also has associatedtherewith a properties correlation descriptor 3611 that describes themembership qualifications for the category node 3610 using theproperties of the candidate member items. When determining whether ornot a member item should be included in a category, the propertiescorrelation descriptor may be used to evaluate the descriptor againstthe properties of the member item.

In a taxonomy, two categories can be related to each other in a numberof different ways. One common relation is that one category is a subsetof another. For example, if there is a “vehicle” category that containsall objects that represent vehicles, there might be a “car” categorythat contains a subset of the vehicles category. The propertycorrelation descriptors of both categories may define the specificrelationship. For instance, the property correlation descriptor for thevehicles category may indicate that objects having the followingproperties will be included in the category: 1) the object is movable,2) the object may contain a human. The car category property correlationdescriptor may include these two property requirements either expresslyor implicitly, and may also include the following propertyrequirements: 1) the object contains at least 3 wheels that maintaincontact with the earth during motion of the object, 2) the object isautomotive, 3) the height of the object does not exceed 6 feet. Based onthe property correlation descriptors for each category, thetaxonomization component may assign an object to one or more categoriesin any given domain-specific taxonomy, and may also understand therelationship between the categories.

In FIG. 36, a second category node 3620 is shown that includes anotherproperty correlation descriptor 3621. The category node 3620 logicallyincludes all member items that satisfy the property correlationdescriptor 3621. In this case, the member items logically included inthe category node are a subset of the member items included in the firstcategory node 3610 (e.g. including member items 3511F, 3511J, 3511N and3511P). This could be because the property correlation descriptor 3621of the second category node 3620 specifies the same propertyrequirements as the correlation descriptor 3611 of the first categorynode 3610, except for one or more additional property requirements. Therelation between the first category node 3610 and the second categorynode 3620 is logically represented by relation 3615.

In a vehicle-car example, the relationship between the categories is asubset relation. That is, one category (e.g., the car category) is asubset of the other (e.g., the vehicle category). However, there are awide variety of other types of relationships as well, even perhaps newrelationships that have never been recognized or used before. Forexample, there might be a majority inheritance relationship in which ifa majority (or some specified percentage) of the objects in one categoryhave a particular property value, the objects in another category havethis property and inherit this property value. There might be a “similarcolor” relationship in which if one category of objects has a primarycolor within a certain wavelength range of visible light, then the othercategory contains objects having a primary color within a certainneighboring wavelength range of visible light. There might be a “virusmutation” relationship in which if one category contains objects thatrepresent certain infectious diseases that are caused primarily by aparticular virus, a related category might include objects thatrepresent certain infectious diseases that are caused by a mutated formof the virus. The examples could go on and on for volumes. One ofordinary skill in the art will recognize after having reviewed thisdescription, that the kinds of relationships between categories is notlimited.

Further a single taxonomy can have many different types of relations.For clarity, various taxonomies will now be described in an abstractsense. Examples of abstractly represented taxonomies are shown in FIGS.37A through 37C. Then specific examples will be described, understandingthat the principles described herein enable countless applications ofdomain-specific taxonomies in a data-driven visualization.

While the example of FIG. 36 is a simple two category node taxonomy, theexamples of FIGS. 37A through 37C are more complex. Each node in thetaxonomies 3700A through 3700C of FIGS. 37A through 37C represents acategory node that contains zero or more member items, and may have aproperty correlation descriptor associated with each that is essentiallyan admission policy for admitting member items into the category node.To avoid undue complexity, however, the member items and propertycorrelation descriptor for each of the category nodes of the taxonomies3700A through 3700C are not illustrated. The lines between the categorynodes represent the relations between category nodes. They might be asubset relation or some other kind of relation without limit. Theprecise nature of the relations between category nodes is not critical.Nevertheless, to emphasize that there may be a variety of relation typesbetween category nodes in the taxonomy, the relations are labeled withan A, B, C, D, or E.

FIGS. 37A through 37C are provided just as an example. The precisestructure of the taxonomies of FIGS. 37A through 37C is not only notcritical, but the principles described herein permit great flexibilityin what kinds of taxonomies can be generated even based on the same setof input candidate member items. In these examples, the taxonomy 3700Aincludes category node 3701A through 3710A related to each other usingrelation types A, B and C. Taxonomy 3700B includes categories 3701Bthrough 3708B related to each other using relation types B, C and D.Taxonomy 3700C includes categories 3701C through 3712C related to eachother using relation types C, D and E. In this example, taxonomies 3700Aand 3700B are hierarchical, whereas taxonomy 3700C is more of anon-hierarchical network.

As new candidate member items become available, those candidate memberitems may be evaluated against the property correlation descriptor ofeach of the category nodes in each the taxonomies. If the properties ofthe member item have values that permit the member item to satisfy therequirements of the property correlation descriptor (i.e., the admissionpolicy), the member item is admitted into the category node. Forinstance, perhaps a pointer to the member item is added to the categorynode. Thus, if new member items have sufficient numbers of properties,new member items may be imported automatically into appropriatecategories in all of the taxonomies.

FIG. 38 illustrates a member item 3800 that includes multiple properties3801. There might be a single property, but there might also bepotentially thousands of properties associated with the member item3800. In FIG. 38, the member item 3800 is illustrated as including fourproperties 3801A, 3801B, 3801C and 3801D, amongst potentially others asrepresented by the ellipsis 3801F. There is no limit to what theseproperties may be. They might be anything that might be useful incategorizing the member item into a taxonomy.

In one embodiment, potential data for each of the data portion 210, theanalytics portion 220, and the view portion 230 may be taxonomized. Forexample, consider the domain in which the author is composing a consumerapplication that allows an individual (such as a consumer orneighborhood resident) to interface with a map of a city.

In this consumer domain, there might be a taxonomy for view data 231that can be selected. For instance, there might be a building categorythat includes all of the buildings. There might be different types ofbuildings: government buildings, hospitals, restaurants, houses, and soforth. There might also be a transit category that includes railroad,roadways, and canals sub-categories. The roadways category might containcategories or objects representing streets, highways, bike-paths,overpasses, and so forth. The streets category might include objects orcategories of visual representations of one way streets, multi-linestreets, turn lanes, center lanes, and so forth. There might be aparking category showing different types of visual representations ofparking or other sub-categories of parking (e.g., multi-level parking,underground parking, street parking, parking lots, and so forth).Parking might also be sub-categorized by whether or not parking is free,or whether there is a cost.

There might also be a taxonomy of the input data in this consumerdomain. For instance, a parking structure might have data associatedwith it such as, for example, 1) whether the parking is valet parking,2) what the hourly change is for the parking, 3) the hours that theparking is open, 4) whether the parking is patrolled by security, and ifso, how many security officers there are per unit area of parking, 5)the number of levels of the parking, 6) the square footage of theparking if there is but one level, and if multi-level parking, thesquare footage on each level, 7) the annualized historical number of carthefts that occur in the parking structure, 8) the volume usage of theparking, 9) whether parking is restricted to the satisfaction of one ormore conditions (i.e., employment at a nearby business, patronage at arestaurant or mall, and so forth), or any other data that might behelpful. There might also be data associated with other visual items aswell, and data that may never affect how the visual items is rendered,but might be used for a calculation at some point.

However, there might also be a taxonomy of the analytics data 221 thatis specific to this consumer map domain. For instance, the analyticsmight present cost-based analytics in one category, time-based analyticsin another category, distance-based analytics in yet another category,directory analytics in another category, and routing analytics inanother category. Here, the analytics are taxonomized to assist theauthor in formulating an analytical model for the desired application.For instance, the routing analytics category might include a categoryfor equations that calculate a route, a constraint that specifies whatrestrictions can be made on the routing (such as shortest route, mostuse of highways, avoid streets, and so forth), or rules (such as trafficdirections on particular roads). Similar subcategories might also beincluded for the other categories as well.

Now consider another domain, also dealing with the layout of a city, butthis time, the domain is city planning. Here, there are analytics thatare interesting to city planners that are of little to no interest to aconsumer. For instance, there might be analytics that calculate howthick the pavement should be given a certain traffic usage, what theoverall installation and maintenance cost per linear foot of a certainroad type placed in a certain area, what the safety factor of a bridgeis given expected traffic patterns forecast for the next 20 years, whatthe traffic bottlenecks are in the current city plan, what theenvironmental impact would be if a particular building was constructedin a particular location, what the impact would be if certainrestrictions were placed on the usage of that particular building and soforth. Here, the problems to be solved are different than those to besolved in the consumer domain. Accordingly, the taxonomy of theanalytics may be laid out much differently for the city planning domainas compared to the consumer domain, even though both deal with a citytopology.

On the other hand, a tractor design domain might be interested in awhole different set of analytics, and would use a different taxonomy.For instance, the visual items of a tractor design domain might betotally different than that of a city planning domain. No longer isthere a concern for city visual elements. Now, the various visualelements that make up a tractor are taxonomized. As an example, thevisual items might be taxonomized using a relation of what can beconnected to what. For instance, there might be a category for “Thingsthat can be connected to the seat”, “Things that can be connected to thecarburetor, “Things that can be connected to the rear axle” and soforth. There might also be a different analytics taxonomy. For instance,there might be a constraint regarding tread depth on the tiresconsidering that the tractor needs to navigate through wet soil. Theremight be analytics that calculate the overall weight of the tractor orits subcomponents, and so forth.

FIG. 39 illustrates a domain-specific taxonomy 3900 and represents oneexample of the domain-specific taxonomies 3521 of FIG. 35. In oneembodiment, the domain-specific taxonomy includes a data taxonomy 3901in which at least some of the available data items are taxonomized intocorresponding related categories, a view component taxonomy 3903 inwhich at least some of the available view components are taxonomizedinto a corresponding related view components categories, and ananalytics taxonomy 3902 in which at least some of the availableanalytics are taxonomized into correlating related analytics categories.Examples of such domain specific taxonomies in which data, analytics,and view components are taxonomized in a manner that is specific todomain have already been described.

FIG. 40 illustrates a method for navigating and using analytics. Theanalytics component 220 is accessed (act 4001) along with thecorresponding domain specific analytics taxonomy (act 4003). If thereare multiple domain-specific analytics taxonomies, the domain may firstbe identified (act 4002), before the domain-specific analytics taxonomymay be accessed (act 4003).

Then, the analytics taxonomy may be navigated (act 4004) by traversingthe related categories. This navigation may be performed by a humanbeing with the assistance of a computing system, or may be performedeven by a computing system alone without the contemporaneous assistanceof a human being. A computer or human may derive information from thecorrelation property descriptor for each category that defines thatadmission policy for analytics to be entered into that category.Information may also be derived by the relationships between categories.The navigation may be used to solve the analytics problem therebysolving for output model parameters, or perhaps for purposes of merginganalytics from multiple models. Alternatively, the navigation may beused to compose the anaytics model in the first place.

For instance, suppose an analytics taxonomy categorizes relations interms of the identity of the type of problem to be solved. The composercould begin by reviewing all of those analytics in the problem typecategory of interest. That category might have related categories thatdefine portions of the problem to be solved. The user could quicklynavigate to those related categories and find analytics that are ofrelevance in the domain.

Search and Exploration

As previously mentioned, the data-driven analytics model may be used toform analytics intensive search and exploration operations. FIG. 41illustrates a flowchart of a method 4100 for searching using thedata-driven analytics model. The method 4100 may be performed each timethe search tool 242 receives or otherwise accesses a search request (act4101).

The principles described herein are not limited to the mechanism thatthe user may use to enter a search request. Nevertheless, a few exampleswill now be provided to show the wide diversity of search request entrymechanisms. In one example, perhaps the search request is text-based andentered directly into a text search field. In another example, perhapsradio buttons are filled in to enter search parameters. Perhaps a slidermight also be used to enter a range for the search parameter. The searchrequest generation may have been generated in an interactive fashionwith the user. For example, in the case where the user requests realestate that experiences a certain noise level range, the applicationmight generate noise that gets increasingly louder, and ask the user tohit the “Too Much Noise” button when the noise gets louder that the userwant to bear.

The search request is not a conventional search request, but may requiresolving operations of the data-driven analytics model. Before solving,however, the search tool 242 of FIG. 2 identifies any model parametersthat should be solved for in order to be able to respond to the request(act 4102). This might be accomplished using, for example, the varioustaxonomies discussed above. For instance, in the case where the user issearching for real estate that is not in the shadow of a mountain after9:15 at any point of the year, there might be a model variable called“mountain shade” that is solved for. In the case where the user searchesfor real estate that experiences certain noise levels, there might be amodel variable called “average noise” that is to be solved for given aparticular coordinate.

Once the relevant output variables are identified, the analyticalrelations of the analytics portion 220 are used to solve for the outputvariables (act 4103). The solved output variable(s) are then used by thesearch tool 242 to formulate a response to the search request using thesolved value(s) (act 4104). Although in some cases the user mightinteract with the method 4100 as the method 4100 is being executed, themethod 4100 may be performed by a computing system withoutcontemporaneous assistance from a human being. The search request may beissued by a user or perhaps even by another computing or softwaremodule.

The method 4100 may be repeated numerous times each time a searchrequest is processed. The model variables that are solved for may, butneed not, be different for each search request. For example, there maybe three search requests for homes that have a certain price range, andnoise levels. For example, there might be a search request for homes inthe $400,000 to $600,000 price range, and whose average noise levels arebelow 50 decibels. The parameters to be solved for here would be thenoise levels. A second search request may be for homes in the $200,000to $500,000 price range, and whose noise levels are below 60 decibels.Here the parameters to be solved for would once again be noise levels.Note, however, that in the second search request, some of the solvingoperations would have already been done for the search request. Forinstance, based on the first search request, the system alreadyidentified homes in the $400,000 to $500,000 price range whose noiselevels were below 50 decibels. Thus, for those houses, there is no needto recalculate the noise levels. Once solved, those values may be keptfor future searching. Thus, this allows a user to perform exploration bysubmitting follow-on requests. The user might then submit a third searchrequest for houses in the $400,000 to $500,000 price range, and havingnoise levels less than 45 decibels. Since noise levels for those homeshave already been solved for, there is no need to solve for them again.Accordingly, the search results can be returned with much lesscomputation. In essence, the system may learn new information by solvingproblems, and be able to take advantage of that new information to solveother problems.

As previously mentioned, each search request might involve solving fordifferent output model variables. For instance, after performing thesearch requests just described, the user might submit a search requestfor houses that are not in the shadow of a mountain. Once the systemsolves for this, whenever this or another user submits a similarrequest, the results from the solve may be used to fulfill thatsubsequent search request. A user might submit a search request forhouses that would stay standing in a magnitude 8.0 earthquake, causing asimulation to verify that each house would either stay standing, fall,or perhaps provide some percentage chance that the house would remainstanding. For houses for which there was insufficient structuralinformation to perform an accurate simulation, the system might simplystate that the results are inconclusive. Once the earthquake simulationsare performed, unless there was some structural change to the house thatrequired re-simulation, or unless there was some improved simulationsolver, the results may be used whenever someone submits a searchrequests for houses that could withstand a certain magnitude ofearthquake. A user might also perform a search request for houses withina certain price range that would not be flooded or destroyed should acategory 5 hurricane occur.

Having described the embodiments in some detail, as a side-note, thevarious operations and structures described herein may, but need not, beimplemented by way of a computing system. Accordingly, to conclude thisdescription, an example computing system will be described with respectto FIG. 25.

FIG. 42 illustrates a computing system 4200. Computing systems are nowincreasingly taking a wide variety of forms. Computing systems may, forexample, be handheld devices, appliances, laptop computers, desktopcomputers, mainframes, distributed computing systems, or even devicesthat have not conventionally been considered a computing system. In thisdescription and in the claims, the term “computing system” is definedbroadly as including any device or system (or combination thereof) thatincludes at least one processor, and a memory capable of having thereoncomputer-executable instructions that may be executed by the processor.The memory may take any form and may depend on the nature and form ofthe computing system. A computing system may be distributed over anetwork environment and may include multiple constituent computingsystems.

As illustrated in FIG. 42, in its most basic configuration, a computingsystem 4200 typically includes at least one processing unit 4202 andmemory 4204. The memory 4204 may be physical system memory, which may bevolatile, non-volatile, or some combination of the two. The term“memory” may also be used herein to refer to non-volatile mass storagesuch as physical storage media. If the computing system is distributed,the processing, memory and/or storage capability may be distributed aswell. As used herein, the term “module” or “component” can refer tosoftware objects or routines that execute on the computing system. Thedifferent components, modules, engines, and services described hereinmay be implemented as objects or processes that execute on the computingsystem (e.g., as separate threads).

In the description that follows, embodiments are described withreference to acts that are performed by one or more computing systems.If such acts are implemented in software, one or more processors of theassociated computing system that performs the act direct the operationof the computing system in response to having executedcomputer-executable instructions. An example of such an operationinvolves the manipulation of data. The computer-executable instructions(and the manipulated data) may be stored in the memory 4204 of thecomputing system 4200.

Computing system 4200 may also contain communication channels 4208 thatallow the computing system 4200 to communicate with other messageprocessors over, for example, network 4210. Communication channels 4208are examples of communications media. Communications media typicallyembody computer-readable instructions, data structures, program modules,or other data in a modulated data signal such as a carrier wave or othertransport mechanism and include any information-delivery media. By wayof example, and not limitation, communications media include wiredmedia, such as wired networks and direct-wired connections, and wirelessmedia such as acoustic, radio, infrared, and other wireless media. Theterm computer-readable media as used herein includes both storage mediaand communications media.

Embodiments within the scope of the present invention also includecomputer-readable media for carrying or having computer-executableinstructions or data structures stored thereon. Such computer-readablemedia can be any available media that can be accessed by a generalpurpose or special purpose computer. By way of example, and notlimitation, such computer-readable media can comprise physical storageand/or memory media such as RAM, ROM, EEPROM, CD-ROM or other opticaldisk storage, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to carry or store desired programcode means in the form of computer-executable instructions or datastructures and which can be accessed by a general purpose or specialpurpose computer. Combinations of the above should also be includedwithin the scope of computer-readable media.

Computer-executable instructions comprise, for example, instructions anddata which cause a general purpose computer, special purpose computer,or special purpose processing device to perform a certain function orgroup of functions. Although the subject matter has been described inlanguage specific to structural features and/or methodological acts, itis to be understood that the subject matter defined in the appendedclaims is not necessarily limited to the specific features or actsdescribed herein. Rather, the specific features and acts describedherein are disclosed as example forms of implementing the claims.

The present invention may be embodied in other specific forms withoutdeparting from its spirit or essential characteristics. The describedembodiments are to be considered in all respects only as illustrativeand not restrictive. The scope of the invention is, therefore, indicatedby the appended claims rather than by the foregoing description. Allchanges which come within the meaning and range of equivalency of theclaims are to be embraced within their scope.

1. A physical computer program product comprising one or more physicalcomputer-readable media having thereon computer-executable instructionsthat are structured such that, when executed by one or more processorsof a computing system, the computing system is caused to infer adependency graph by performing the following: an act of identifying oneor more input model parameter(s) for a particular solve; an act ofidentifying one or more output model parameter(s) for the particularsolve, wherein the identity of the input model parameter(s) or theoutput model parameter(s) may change from one solve to the next; an actof identifying a first model analytical expression that will allow asolve for at least one of the output model parameter(s) using a firstset of one or more other parameters, the first model analyticalexpression selected from a pool of model analytical expressionscorresponding to an analytical model; an act of formulating at least apart of a dependency graph that identifies that the first set of one ormore parameters in conjunction with the identified first modelanalytical expression leads to the at least one of the output modelparameter(s), wherein at least one of the first set of one or moreparameters is an intermediate model parameter, and not one of the inputmodel parameter(s); an act of identifying a second model analyticalexpressions that will allow for a solve for the intermediate modelparameter given a second set of one or more parameters, and an act ofextending the dependency graph to reflect that the second set of one ormore parameters in conjunction with the identified second modelanalytical expression leads to the intermediate model parameter; and ifthere is any in the second set of one or more parameters that is anintermediate model parameter, an act of recursively repeating the act ofidentifying the analytics and extending the dependency graph until thedependency graph shows a solve dependency that leads from the identifiedone or more input model parameter(s) to the identified one or moreoutput model parameter(s) through a plurality of intermediate modelparameters for the particular solve.
 2. The physical computer programproduct in accordance with claim 1, wherein the dependency graphincludes a plurality of nodes representing parameters, and includes aplurality of links representing analytical relations between theparameters.
 3. The physical computer program product in accordance withclaim 2, wherein the computer-executable instructions are structuredsuch that the computing system provides a user interface in which theuser can view properties associated with at least some nodes in thedependency graph.
 4. The physical computer program product in accordancewith claim 3, wherein for at least one of the at least some nodes in thedependency graph, the properties include a value of a parameterassociated with the node.
 5. The physical computer program product inaccordance with claim 4, wherein for the at least one of the at leastsome nodes in the dependency graph, the properties also include anidentity of a parameter associated with the node.
 6. The physicalcomputer program product in accordance with claim 3, wherein for atleast one of the at least some nodes in the dependency graph, theproperties include an identity of a parameter associated with the node.7. The physical computer program product in accordance with claim 6,wherein the computer-executable instructions are structured such thatthe computing system provides a user interface in which the user canview properties associated with at least some links in the dependencygraph.
 8. The physical computer program product in accordance with claim7, wherein at least one property associated with the at least some ofthe links includes an analytical expression.
 9. The physical computerprogram product in accordance with claim 2, wherein thecomputer-executable instructions are structured such that the computingsystem provides a user interface in which the user can view propertiesassociated with at least some links in the dependency graph.
 10. Thephysical computer program product in accordance with claim 9, wherein atleast one property associated with the at least some of the linksincludes an analytical expression.
 11. The physical computer programproduct in accordance with claim 2, wherein the computer-executableinstructions are structured such that the computing system provides auser interface in which the user can edit properties associated with atleast some nodes in the dependency graph.
 12. The physical computerprogram product in accordance with claim 11, wherein thecomputer-executable instructions are structured such that the computingsystem provides a user interface in which the user can edit propertiesassociated with at least some links in the dependency graph.
 13. Thephysical computer program product in accordance with claim 2, whereinthe computer-executable instructions are structured such that thecomputing system provides a user interface in which the user can editproperties associated with at least some links in the dependency graph.14. The physical computer program product in accordance with claim 2,wherein the computer-executable instructions are structured such thatthe computing system provides a user interface in which the user canedit a structure of the dependency graph.
 15. A method for inferring adependency graph, the method comprising the following: an act of aphysical computing system identifying one or more input modelparameter(s) for a particular solve to be performed by the physicalcomputing system; an act of the physical computing system identifyingone or more output model parameter(s) for the particular solve, whereinthe identity of the input model parameter(s) or the output modelparameter(s) may change from one solve to the next; an act of thephysical computing system identifying a first model analyticalexpression that will allow a solve for at least one of the output modelparameter(s) using a first set of one or more other parameters, thefirst model analytical expression selected from a pool of modelanalytical expressions corresponding to an analytical model; an act ofthe physical computing system formulating at least a part of adependency graph that identifies that the first set of one or moreparameters in conjunction with the identified first model analyticalexpression leads to the at least one of the output model parameter(s),wherein at least one of the first set of one or more parameters is anintermediate model parameter, and not one of the input modelparameter(s); an act of the physical computing system identifying asecond model analytical expressions that will allow for a solve for theintermediate model parameter given a second set of one or moreparameters, and an act of extending the dependency graph to reflect thatthe second set of one or more parameters in conjunction with theidentified second model analytical expression leads to the intermediatemodel parameter; and if there is any in the second set of one or moreparameters that is an intermediate model parameter, an act of thecomputing system recursively repeating the act of identifying theanalytics and extending the dependency graph until the dependency graphshows a solve dependency that leads from the identified one or moreinput model parameter(s) to the identified one or more output modelparameter(s) through a plurality of intermediate model parameters forthe particular solve.
 16. The method in accordance with claim 15,wherein the dependency graph includes a plurality of nodes representingparameters, and includes a plurality of links representing analyticalrelations between the parameters.
 17. The method in accordance withclaim 16, wherein the computing system provides a user interface inwhich the user can view or edit properties associated with at least somenodes in the dependency graph.
 18. The method in accordance with claim17, wherein the computing system provides a user interface in which theuser can view or edit properties associated with at least some links inthe dependency graph.
 19. The method in accordance with claim 16,wherein the computing system provides a user interface in which the usercan view or edit properties associated with at least some links in thedependency graph.
 20. A physical computer program product comprising oneor more physical computer-readable media having thereoncomputer-executable instructions that are structured such that, whenexecuted by one or more processors of a computing system, the computingsystem is caused to infer a dependency graph by performing thefollowing: an act of identifying one or more input model parameter(s)for a particular solve; an act of identifying one or more output modelparameter(s) for the particular solve, wherein the identity of the inputmodel parameter(s) or the output model parameter(s) may change from onesolve to the next; an act of identifying a first model analyticalexpression that will allow a solve for at least one of the output modelparameter(s) using a first set of one or more other parameters, thefirst model analytical expression selected from a pool of modelanalytical expressions corresponding to an analytical model; an act offormulating at least a part of a dependency graph that identifies thatthe first set of one or more parameters in conjunction with theidentified first model analytical expression leads to the at least oneof the output model parameter(s), wherein at least one of the first setof one or more parameters is an intermediate model parameter, and notone of the input model parameter(s); an act of identifying a secondmodel analytical expressions that will allow for a solve for theintermediate model parameter given a second set of one or moreparameters, and an act of extending the dependency graph to reflect thatthe second set of one or more parameters in conjunction with theidentified second model analytical expression leads to the intermediatemodel parameter; and if there is any in the second set of one or moreparameters that is an intermediate model parameter, an act ofrecursively repeating the act of identifying the analytics and extendingthe dependency graph until the dependency graph shows a solve dependencythat leads from the identified one or more input model parameter(s) tothe identified one or more output model parameter(s) through a pluralityof intermediate model parameters for the particular solve, wherein thedependency graph includes a plurality of nodes representing parameters,and includes a plurality of links representing analytical relationsbetween the parameters, wherein the computer-executable instructions arestructured such that the computing system provides a user interface inwhich the user can view or edit properties associated with at least somenodes in the dependency graph, and can view or edit propertiesassociated with at least some links in the dependency graph.